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create_table_from_conda_build.py
from conda_build import api
from conda_build.config import Config
import jinja2
import os
import sys
import ruamel_yaml
from tempfile import TemporaryDirectory
HTML = '''
<!DOCTYPE html>
<html>
<body>
<p>Number of supported packages: {{ pkgs|length }} </p>
<table border="1" class="docutils">
<tr class="row-odd">
<th>Name</th>
<th>Version</th>
<th>Summary</th>
<th>License</th>
</tr>
{% for key, item in pkgs.items() %}
<tr>
<td>{{ item.pkg_name }}</td>
<td>{{ item.pkg_version }}</td>
<td>{{ item.summary }}</td>
<td>{{ item.license }}</td>
</tr>
{% endfor %}
</table>
</body>
</html>
'''
def render(yamls, output_filename):
pkgs = {}
print(sys.argv[1])
with TemporaryDirectory() as td, open(output_filename, 'w') as outfile:
for i, yaml in enumerate(yamls):
print("doing %s" % yaml)
variant = {'python': '3.6.3',
'ipython': '6.0',
'r': '3.4.2',
'r_base': '3.4.2'}
config = Config(variant=variant)
result, _, _ = api.render(yaml, no_download_source=False, config=config)[0]
try:
summary = result.meta['about']['summary']
except:
summary = "UNKNOWN"
try:
license = result.meta['about']['license_family']
except:
print("license failed for %s" % yaml)
license = result.meta['about']['license']
pkgs[i] = {'pkg_name': result.meta['package']['name'],
'pkg_version': result.meta['package']['version'],
'summary': summary,
'license': license}
outfile.write(jinja2.Environment().from_string(HTML).render(pkgs=pkgs))
def main(output_filename, recipes_dir):
yamldirs = []
dir = os.path.expanduser(recipes_dir)
dir = dir if os.path.isdir(dir) else os.path.dirname(dir)
for root, dirs, files in os.walk(dir):
yamldirs.extend([root for file in files if file.endswith('.yaml')])
render(sorted(yamldirs[1:]), output_filename=output_filename)
if __name__ == '__main__':
if len(sys.argv) < 2:
print("ERROR: Please pass a single argument for the output file (and run in a directory containing R recipes")
sys.exit(1)
main(sys.argv[1], os.getcwd())
Raw
r_3_4_2-packages.html
<!DOCTYPE html>
<html>
<body>
<p>Number of supported packages: 328 </p>
<table border="1" class="docutils">
<tr class="row-odd">
<th>Name</th>
<th>Version</th>
<th>Summary</th>
<th>License</th>
</tr>
<tr>
<td>r-acepack</td>
<td>1.4.1</td>
<td>Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-adgoftest</td>
<td>0.3</td>
<td>Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"</td>
<td>GPL</td>
</tr>
<tr>
<td>r-aer</td>
<td>1.2_5</td>
<td>Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-afex</td>
<td>0.18_0</td>
<td>Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed between-within (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex uses type 3 sums of squares as default (imitating commercial statistical software).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-anomalydetection</td>
<td>0.2.4</td>
<td>Implements procedures to aid in detecting network log anomalies. By combining various multivariate analytic approaches relevant to network anomaly detection, it provides cyber analysts efficient means to detect suspected anomalies requiring further evaluation.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-ape</td>
<td>4.1</td>
<td>Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-argparse</td>
<td>1.0.7</td>
<td>A command line parser to be used with Rscript to write "#!" shebang scripts that gracefully accept positional and optional arguments and automatically generate usage.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-assertthat</td>
<td>0.2.0</td>
<td>assertthat is an extension to stopifnot() that makes it easy to declare the pre and post conditions that you code should satisfy, while also producing friendly error messages so that your users know what they've done wrong.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-backports</td>
<td>1.1.1</td>
<td>Implementations of functions which have been introduced in R since version 3.0.0. The backports are conditionally exported which results in R resolving the function names to the version shipped with R (if available) and uses the implemented backports as fallback. This way package developers can make use of the new functions without worrying about the minimum required R version.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-base</td>
<td>3.4.2</td>
<td>R is a free software environment for statistical computing and graphics.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-base64enc</td>
<td>0.1_3</td>
<td>This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-bcp</td>
<td>4.0.0</td>
<td>Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo. It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-bdsmatrix</td>
<td>1.3_2</td>
<td>This is a special case of sparse matrices, used by coxme </td>
<td>LGPL</td>
</tr>
<tr>
<td>r-bestglm</td>
<td>0.36</td>
<td>Best subset glm using information criteria or cross-validation.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-bh</td>
<td>1.65.0_1</td>
<td>Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN package. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.65.0-1, the following Boost libraries are included: 'algorithm' 'align' 'any' 'atomic' 'bimap' 'bind' 'circular_buffer' 'concept' 'config' 'container' 'date'_'time' 'detail' 'dynamic_bitset' 'exception' 'filesystem' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'math' 'move' 'mpl' 'multiprcecision' 'numeric' 'pending' 'phoenix' 'preprocessor' 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.</td>
<td>OTHER</td>
</tr>
<tr>
<td>r-bindr</td>
<td>0.1</td>
<td>Provides a simple interface for creating active bindings where the bound function accepts additional arguments.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-bindrcpp</td>
<td>0.2</td>
<td>Provides an easy way to fill an environment with active bindings that call a C++ function.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-bit</td>
<td>1.1_12</td>
<td>bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With 'bit' vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM and ~ factor 32 more speed on boolean operations. Due to overhead of R calls, actual speed gain depends on the size of the vector: expect gains for vectors of size > 10000 elements. Even for one-time boolean operations it can pay-off to convert to bit, the pay-off is obvious, when such components are used more than once. Reading from and writing to bit is approximately as fast as accessing standard logicals - mostly due to R's time for memory allocation. The package allows to work with pre-allocated memory for return values by calling .Call() directly: when evaluating the speed of C-access with pre-allocated vector memory, coping from bit to logical requires only 70% of the time for copying from logical to logical; and copying from logical to bit comes at a performance penalty of 150%. the package now contains further classes for representing logical selections: 'bitwhich' for very skewed selections and 'ri' for selecting ranges of values for chunked processing. All three index classes can be used for subsetting 'ff' objects (ff-2.1-0 and higher).</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-bit64</td>
<td>0.9_7</td>
<td> Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in +-2^63. WARNING: do not use them as replacement for 32bit integers, integer64 are not supported for subscripting by R-core and they have different semantics when combined with double, e.g. integer64 + double => integer64. Class integer64 can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as 'match' and 'order' support inter- active data exploration and manipulation and optionally leverage caching.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-bitops</td>
<td>1.0_6</td>
<td>Functions for bitwise operations on integer vectors.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-blob</td>
<td>1.1.0</td>
<td>R's raw vector is useful for storing a single binary object. What if you want to put a vector of them in a data frame? The blob package provides the blob object, a list of raw vectors, suitable for use as a column in data frame.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-boot</td>
<td>1.3_20</td>
<td>Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-bradleyterry2</td>
<td>1.0_8</td>
<td>Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-brew</td>
<td>1.0_6</td>
<td>brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-brglm</td>
<td>0.6.1</td>
<td>Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-broom</td>
<td>0.4.2</td>
<td>Convert statistical analysis objects from R into tidy data frames, so that they can more easily be combined, reshaped and otherwise processed with tools like 'dplyr', 'tidyr' and 'ggplot2'. The package provides three S3 generics: tidy, which summarizes a model's statistical findings such as coefficients of a regression; augment, which adds columns to the original data such as predictions, residuals and cluster assignments; and glance, which provides a one-row summary of model-level statistics.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-cairo</td>
<td>1.5_9</td>
<td>Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-car</td>
<td>2.1_5</td>
<td> Functions and Datasets to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Second Edition, Sage, 2011.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-caret</td>
<td>6.0_77</td>
<td>Misc functions for training and plotting classification and regression models.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-catools</td>
<td>1.17.1</td>
<td>Contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-cellranger</td>
<td>1.1.0</td>
<td>Helper functions to work with spreadsheets and the "A1:D10" style of cell range specification.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-checkmate</td>
<td>1.8.4</td>
<td>Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-chron</td>
<td>2.3_51</td>
<td>Provides chronological objects which can handle dates and times.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-class</td>
<td>7.3_14</td>
<td>Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-cluster</td>
<td>2.0.6</td>
<td>Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-coda</td>
<td>0.19_1</td>
<td>Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-codetools</td>
<td>0.2_15</td>
<td>Code analysis tools for R.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-coin</td>
<td>1.2_1</td>
<td>Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-colorspace</td>
<td>1.3_2</td>
<td>Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with an interactive palette picker (with either a Tcl/Tk or a shiny GUI).</td>
<td>BSD</td>
</tr>
<tr>
<td>r-commonmark</td>
<td>1.4</td>
<td>The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-config</td>
<td>0.2</td>
<td>Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-copula</td>
<td>0.999_18</td>
<td>Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-crayon</td>
<td>1.3.4</td>
<td>Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI' color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the 'chalk' 'JavaScript' project.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-crosstalk</td>
<td>1.0.0</td>
<td>Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-ctv</td>
<td>0.8_3</td>
<td>Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-curl</td>
<td>3.0</td>
<td>The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption (https, ftps), gzip compression, authentication, and other 'libcurl' goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly web client see the 'httr' package which builds on this package with http specific tools and logic.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-cvst</td>
<td>0.2_1</td>
<td>This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-cvtools</td>
<td>0.3.2</td>
<td>Tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-data.table</td>
<td>1.10.4_1</td>
<td>Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, a fast friendly file reader and parallel file writer. Offers a natural and flexible syntax, for faster development.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-dbi</td>
<td>0.7</td>
<td>A database interface definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-dbplyr</td>
<td>1.1.0</td>
<td>A 'dplyr' back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a 'DBI' back end; more advanced features require 'SQL' translation to be provided by the package author.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-ddalpha</td>
<td>1.3.1</td>
<td>Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-debugme</td>
<td>1.0.2</td>
<td>Specify debug messages as special string constants, and control debugging of packages via environment variables.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-deoptimr</td>
<td>1.0_8</td>
<td>Differential Evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it only provides an implementation of the 'jDE' algorithm by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-desc</td>
<td>1.1.1</td>
<td>Tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-devtools</td>
<td>1.13.3</td>
<td>Collection of package development tools.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-diagrammer</td>
<td>0.9.2</td>
<td>Graph and network visualization using tabular data.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-dichromat</td>
<td>2.0_0</td>
<td>Collapse red-green or green-blue distinctions to simulate the effects of different types of color-blindness.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-digest</td>
<td>0.6.12</td>
<td>Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R language objects, as well as a function 'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-dimred</td>
<td>0.1.0</td>
<td>A collection of dimensionality reduction techniques from R packages and provides a common interface for calling the methods.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-domc</td>
<td>1.3.4</td>
<td>Provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-doparallel</td>
<td>1.0.11</td>
<td>Provides a parallel backend for the %dopar% function using the parallel package.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-downloader</td>
<td>0.4</td>
<td>Provides a wrapper for the download.file function, making it possible to download files over HTTPS on Windows, Mac OS X, and other Unix-like platforms. The 'RCurl' package provides this functionality (and much more) but can be difficult to install because it must be compiled with external dependencies. This package has no external dependencies, so it is much easier to install.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-dplyr</td>
<td>0.7.4</td>
<td>A fast, consistent tool for working with data frame like objects, both in memory and out of memory.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-drr</td>
<td>0.0.2</td>
<td>An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-dt</td>
<td>0.2</td>
<td>Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-dygraphs</td>
<td>1.1.1.4</td>
<td>An R interface to the 'dygraphs' JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-e1071</td>
<td>1.6_8</td>
<td>Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-essentials</td>
<td>1.7.0</td>
<td>Some essential packages for working with R</td>
<td>Other</td>
</tr>
<tr>
<td>r-estimability</td>
<td>1.2</td>
<td>Provides tools for determining estimability of linear functions of regression coefficients, and 'epredict' methods that handle non-estimable cases correctly.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-evaluate</td>
<td>0.10.1</td>
<td>Parsing and evaluation tools that make it easy to recreate the command line behaviour of R.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-feather</td>
<td>0.3.1</td>
<td>Read and write feather files, a lightweight binary columnar data store designed for maximum speed.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r</td>
<td>3.4.2</td>
<td>R is a free software environment for statistical computing and graphics.</td>
<td>GPL-3.0</td>
</tr>
<tr>
<td>r-findpython</td>
<td>1.0.2</td>
<td>Package designed to find an acceptable python binary.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-forcats</td>
<td>0.2.0</td>
<td>Helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, 'anonymising', and manually 'recoding').</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-foreach</td>
<td>1.4.3</td>
<td>Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-forecast</td>
<td>8.2</td>
<td>Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-foreign</td>
<td>0.8_69</td>
<td>Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-formatr</td>
<td>1.5</td>
<td>Provides a function tidy_source() to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package (see tidy_app()).</td>
<td>GPL</td>
</tr>
<tr>
<td>r-formattable</td>
<td>0.2.0.1</td>
<td>Provides functions to create formattable vectors and data frames. 'Formattable' vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-formula</td>
<td>1.2_2</td>
<td>Infrastructure for extended formulas with multiple parts on the right-hand side and/or multiple responses on the left-hand side (see <DOI:10.18637/jss.v034.i01>).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-fracdiff</td>
<td>1.4_2</td>
<td>Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-functional</td>
<td>0.6</td>
<td>Curry, Compose, and other higher-order functions</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-fwdselect</td>
<td>2.1.0</td>
<td>A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric).</td>
<td>MIT</td>
</tr>
<tr>
<td>r-gdata</td>
<td>2.18.0</td>
<td>Various R programming tools for data manipulation, including: - medical unit conversions ('ConvertMedUnits', 'MedUnits'), - combining objects ('bindData', 'cbindX', 'combine', 'interleave'), - character vector operations ('centerText', 'startsWith', 'trim'), - factor manipulation ('levels', 'reorder.factor', 'mapLevels'), - obtaining information about R objects ('object.size', 'elem', 'env', 'humanReadable', 'is.what', 'll', 'keep', 'ls.funs', 'Args','nPairs', 'nobs'), - manipulating MS-Excel formatted files ('read.xls', 'installXLSXsupport', 'sheetCount', 'xlsFormats'), - generating fixed-width format files ('write.fwf'), - extricating components of date & time objects ('getYear', 'getMonth', 'getDay', 'getHour', 'getMin', 'getSec'), - operations on columns of data frames ('matchcols', 'rename.vars'), - matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'), - operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'), - operations on data frames ('frameApply', 'wideByFactor'), - value of last evaluated expression ('ans'), and - wrapper for 'sample' that ensures consistent behavior for both scalar and vector arguments ('resample').</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-getopt</td>
<td>1.20.0</td>
<td>Package designed to be used with Rscript to write ``#!'' shebang scripts that accept short and long flags/options. Many users will prefer using instead the packages optparse or argparse which add extra features like automatically generated help option and usage, support for default values, positional argument support, etc.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-ggplot2</td>
<td>2.2.1</td>
<td>A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-ggvis</td>
<td>0.4.3</td>
<td>An implementation of an interactive grammar of graphics, taking the best parts of 'ggplot2', combining them with the reactive framework of 'shiny' and drawing web graphics using 'vega'.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-gistr</td>
<td>0.4.0</td>
<td>Work with 'GitHub' 'gists' from 'R' (e.g., <http://en.wikipedia.org/wiki/GitHub#Gist>, <https://help.github.com/articles/about-gists/>). A 'gist' is simply one or more files with code/text/images/etc. This package allows the user to create new 'gists', update 'gists' with new files, rename files, delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists', open a 'gist' in your default browser, get embed code for a 'gist', list 'gist' 'commits', and get rate limit information when 'authenticated'. Some requests require authentication and some do not. 'Gists' website: <https://gist.github.com/>.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-git2r</td>
<td>0.19.0</td>
<td>Interface to the 'libgit2' library, which is a pure C implementation of the 'Git' core methods. Provides access to 'Git' repositories to extract data and running some basic 'Git' commands.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-glmnet</td>
<td>2.0_13</td>
<td>Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-glue</td>
<td>1.1.1</td>
<td>An implementation of interpreted string literals, inspired by Python's Literal String Interpolation <https://www.python.org/dev/peps/pep-0498/> and Docstrings <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted String Literals <https://docs.julialang.org/en/stable/manual/strings/#triple-quoted-string-literals>.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-gmp</td>
<td>0.5_13.1</td>
<td>Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic).</td>
<td>GPL</td>
</tr>
<tr>
<td>r-gower</td>
<td>0.1.2</td>
<td>Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-gplots</td>
<td>3.0.1</td>
<td>Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary function as ('bandplot', 'wapply'), - enhanced versions of standard plots ('barplot2', 'boxplot2', 'heatmap.2', 'smartlegend'), - manipulating colors ('col2hex', 'colorpanel', 'redgreen', 'greenred', 'bluered', 'redblue', 'rich.colors'), - calculating and plotting two-dimensional data summaries ('ci2d', 'hist2d'), - enhanced regression diagnostic plots ('lmplot2', 'residplot'), - formula-enabled interface to 'stats::lowess' function ('lowess'), - displaying textual data in plots ('textplot', 'sinkplot'), - plotting a matrix where each cell contains a dot whose size reflects the relative magnitude of the elements ('balloonplot'), - plotting "Venn" diagrams ('venn'), - displaying Open-Office style plots ('ooplot'), - plotting multiple data on same region, with separate axes ('overplot'), - plotting means and confidence intervals ('plotCI', 'plotmeans'), - spacing points in an x-y plot so they don't overlap ('space').</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-gridbase</td>
<td>0.4_7</td>
<td>Integration of base and grid graphics</td>
<td>GPL</td>
</tr>
<tr>
<td>r-gridextra</td>
<td>2.3</td>
<td>Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-grpreg</td>
<td>3.1_2</td>
<td>Efficient algorithms for fitting the regularization path of linear or logistic regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-gsl</td>
<td>1.9_10.3</td>
<td> An R wrapper for the special functions and quasi random number generators of the Gnu Scientific Library (http://www.gnu.org/software/gsl/). See gsl-package.Rd for details of overall package organization, and Misc.Rd for some functions that are widely used in the package, and some tips on installation.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-gsw</td>
<td>1.0_5</td>
<td>Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C library, version 3.05-4 (commit '5b4d959e54031f9e972f3e863f63e67fa4f5bfec', dated 2017-08-07, available at <https://github.com/TEOS-10/GSW-C>, which stems from 'Matlab' and other code written by members of Working Group 127 of 'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-gtable</td>
<td>0.2.0</td>
<td>Tools to make it easier to work with "tables" of 'grobs'.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-gtools</td>
<td>3.5.0</td>
<td>Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP\_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-haven</td>
<td>1.1.0</td>
<td>Import foreign statistical formats into R via the embedded 'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-hexbin</td>
<td>1.27.1</td>
<td>Binning and plotting functions for hexagonal bins. Now uses and relies on grid graphics and formal (S4) classes and methods.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-highcharter</td>
<td>0.5.0</td>
<td>A wrapper for the 'Highcharts' library including shortcut functions to plot R objects. 'Highcharts' <http://www.highcharts.com/> is a charting library offering numerous chart types with a simple configuration syntax.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-highr</td>
<td>0.6</td>
<td>Provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. Source code of other languages is supported via Andre Simon's highlight package (http://www.andre-simon.de).</td>
<td>GPL</td>
</tr>
<tr>
<td>r-hmisc</td>
<td>4.0_3</td>
<td>Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-hms</td>
<td>0.3</td>
<td>Implements an S3 class for storing and formatting time-of-day values, based on the 'difftime' class.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-htmltable</td>
<td>1.9</td>
<td>Tables with state-of-the-art layout elements such as row spanners, column spanners, table spanners, zebra striping, and more. While allowing advanced layout, the underlying css-structure is simple in order to maximize compatibility with word processors such as 'MS Word' or 'LibreOffice'. The package also contains a few text formatting functions that help outputting text compatible with HTML/'LaTeX'.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-htmltools</td>
<td>0.3.6</td>
<td>Tools for HTML generation and output.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-htmlwidgets</td>
<td>0.9</td>
<td>A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-httpuv</td>
<td>1.3.5</td>
<td>Provides low-level socket and protocol support for handling HTTP and WebSocket requests directly from within R. It is primarily intended as a building block for other packages, rather than making it particularly easy to create complete web applications using httpuv alone. httpuv is built on top of the libuv and http-parser C libraries, both of which were developed by Joyent, Inc. (See LICENSE file for libuv and http-parser license information.)</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-httr</td>
<td>1.3.1</td>
<td>Useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on).</td>
<td>MIT</td>
</tr>
<tr>
<td>r-hunspell</td>
<td>2.6</td>
<td>A spell checker and morphological analyzer library designed for languages with rich morphology and complex word compounding or character encoding. The package can check and analyze individual words as well as search for incorrect words within a text, latex, html or xml document. Use the 'devtools' package to spell check R documentation with 'hunspell'.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-igraph</td>
<td>1.1.2</td>
<td>Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-influencer</td>
<td>0.1.0</td>
<td>Provides functionality to compute various node centrality measures on networks. Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of Burt's constraint and effective network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's bridging metric. On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-inline</td>
<td>0.3.14</td>
<td>Functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-ipred</td>
<td>0.9_6</td>
<td>Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error. </td>
<td>GPL3</td>
</tr>
<tr>
<td>r-irdisplay</td>
<td>0.4.4</td>
<td> An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>. Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-irkernel</td>
<td>0.8.9</td>
<td>The R kernel for the 'Jupyter' environment executes R code which the front-end ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-irlba</td>
<td>2.2.1</td>
<td>Fast and memory efficient methods for truncated singular value decomposition and principal components analysis of large sparse and dense matrices.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-iterators</td>
<td>1.0.8</td>
<td>Support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-janeaustenr</td>
<td>0.1.5</td>
<td>Full texts for Jane Austen's 6 completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion".</td>
<td>MIT</td>
</tr>
<tr>
<td>r-jpeg</td>
<td>0.1_8</td>
<td>This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-jsonlite</td>
<td>1.5</td>
<td>A fast JSON parser and generator optimized for statistical data and the web. Started out as a fork of 'RJSONIO', but has been completely rewritten in recent versions. The package offers flexible, robust, high performance tools for working with JSON in R and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-kernlab</td>
<td>0.9_25</td>
<td>Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-kernsmooth</td>
<td>2.23_15</td>
<td>Functions for kernel smoothing (and density estimation) corresponding to the book: Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".</td>
<td>MIT</td>
</tr>
<tr>
<td>r-knitr</td>
<td>1.17</td>
<td>Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-kohonen</td>
<td>3.0.4</td>
<td>Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-labeling</td>
<td>0.3</td>
<td>Provides a range of axis labeling algorithms</td>
<td>MIT</td>
</tr>
<tr>
<td>r-lahman</td>
<td>6.0_0</td>
<td>Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2015, as recorded in the 2016 version of the database.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-lars</td>
<td>1.2</td>
<td>Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-lattice</td>
<td>0.20_35</td>
<td>A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-latticeextra</td>
<td>0.6_28</td>
<td>Building on the infrastructure provided by the lattice package, this package provides several new high-level functions and methods, as well as additional utilities such as panel and axis annotation functions.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lava</td>
<td>1.5.1</td>
<td>A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lazyeval</td>
<td>0.2.0</td>
<td>An alternative approach to non-standard evaluation using formulas. Provides a full implementation of LISP style 'quasiquotation', making it easier to generate code with other code.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-leaflet</td>
<td>1.1.0</td>
<td>Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-leaps</td>
<td>3.0</td>
<td>Regression subset selection, including exhaustive search.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lintr</td>
<td>1.0.1</td>
<td>Checks adherence to a given style, syntax errors and possible semantic issues. Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text' and 'Atom'.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-lme4</td>
<td>1.1_14</td>
<td>Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lmertest</td>
<td>2.0_33</td>
<td>Different kinds of tests for linear mixed effects models as implemented in 'lme4' package are provided. The tests comprise types I - III F tests for fixed effects, LR tests for random effects. The package also provides the calculation of population means for fixed factors with confidence intervals and corresponding plots. Finally the backward elimination of non-significant effects is implemented.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lmtest</td>
<td>0.9_35</td>
<td>A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-logging</td>
<td>0.7_103</td>
<td>logging is a pure R package that implements the ubiquitous log4j package.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lsmeans</td>
<td>2.27_2</td>
<td>Obtain least-squares means for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-lubridate</td>
<td>1.6.0</td>
<td>Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The 'lubridate' package has a consistent and memorable syntax that makes working with dates easy and fun.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-magrittr</td>
<td>1.5</td>
<td>Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, "Ceci n'est pas un pipe."</td>
<td>MIT</td>
</tr>
<tr>
<td>r-manipulate</td>
<td>1.0.1</td>
<td>Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. When a value is changed using its corresponding control the expression is automatically re-executed and the plot is redrawn.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-mapproj</td>
<td>1.2_5</td>
<td>Converts latitude/longitude into projected coordinates.</td>
<td>OTHER</td>
</tr>
<tr>
<td>r-maps</td>
<td>3.2.0</td>
<td>Display of maps. Projection code and larger maps are in separate packages ('mapproj' and 'mapdata').</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-maptools</td>
<td>0.9_2</td>
<td>Set of tools for manipulating and reading geographic data, in particular 'ESRI Shapefiles'; C code used from 'shapelib'. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat', 'maps', 'RArcInfo', 'Stata tmap', 'WinBUGS', 'Mondrian', and others.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-markdown</td>
<td>0.8</td>
<td>Provides R bindings to the 'Sundown' 'Markdown' rendering library (https://github.com/vmg/sundown). 'Markdown' is a plain-text formatting syntax that can be converted to 'XHTML' or other formats. See http://en.wikipedia.org/wiki/Markdown for more information about 'Markdown'.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-mass</td>
<td>7.3_47</td>
<td>Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-matrix</td>
<td>1.2_11</td>
<td>Classes and methods for dense and sparse matrices and operations on them using 'LAPACK' and 'SuiteSparse'.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-matrixmodels</td>
<td>0.4_1</td>
<td>Modelling with sparse and dense 'Matrix' matrices, using modular prediction and response module classes.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-memoise</td>
<td>1.1.0</td>
<td>Cache the results of a function so that when you call it again with the same arguments it returns the pre-computed value.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-mgcv</td>
<td>1.8_22</td>
<td>Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar. Includes a gam() function, a wide variety of smoothers, JAGS support and distributions beyond the exponential family.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-microbenchmark</td>
<td>1.4_2.1</td>
<td>Provides infrastructure to accurately measure and compare the execution time of R expressions.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-mime</td>
<td>0.5</td>
<td>Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-miniui</td>
<td>0.1.1</td>
<td>Provides UI widget and layout functions for writing Shiny apps that work well on small screens.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-minqa</td>
<td>1.2.4</td>
<td>Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-mlmrev</td>
<td>1.0_6</td>
<td>Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-mnormt</td>
<td>1.5_5</td>
<td>Functions are provided for computing the density and the distribution function of multivariate normal and "t" random variables, and for generating random vectors sampled from these distributions. Probabilities are computed via non-Monte Carlo methods; different routines are used in the case d=1, d=2, d>2, if d denotes the number of dimensions.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-modelmetrics</td>
<td>1.1.0</td>
<td>Collection of metrics for evaluating models written in C++ using 'Rcpp'.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-modelr</td>
<td>0.1.1</td>
<td>Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-modeltools</td>
<td>0.2_21</td>
<td>A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome!</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-mongolite</td>
<td>1.2</td>
<td>High-performance 'MongoDB' client based on 'libmongoc' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication. The online user manual provides an overview of the available methods in the package: <https://jeroen.github.io/mongolite/>.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-multcomp</td>
<td>1.4_7</td>
<td>Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-munsell</td>
<td>0.4.3</td>
<td>Provides easy access to, and manipulation of, the Munsell colours. Provides a mapping between Munsell's original notation (e.g. "5R 5/10") and hexadecimal strings suitable for use directly in R graphics. Also provides utilities to explore slices through the Munsell colour tree, to transform Munsell colours and display colour palettes.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-mvtnorm</td>
<td>1.0_6</td>
<td>Computes multivariate normal and t probabilities, quantiles, random deviates and densities.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-networkd3</td>
<td>0.4</td>
<td>Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-nlme</td>
<td>3.1_131</td>
<td>Fit and compare Gaussian linear and nonlinear mixed-effects models.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-nloptr</td>
<td>1.0.4</td>
<td> nloptr is an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction for more information on the available algorithms. During installation on Unix the NLopt code is downloaded and compiled from the NLopt website.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-nlp</td>
<td>0.1_11</td>
<td>Basic classes and methods for Natural Language Processing.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-nmf</td>
<td>0.20.6</td>
<td>Provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-nnet</td>
<td>7.3_12</td>
<td>Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-numderiv</td>
<td>2016.8_1</td>
<td>Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions. </td>
<td>GPL2</td>
</tr>
<tr>
<td>r-nycflights13</td>
<td>0.2.2</td>
<td>Airline on-time data for all flights departing NYC in 2013. Also includes useful 'metadata' on airlines, airports, weather, and planes.</td>
<td>OTHER</td>
</tr>
<tr>
<td>r-oce</td>
<td>0.9_22</td>
<td>Supports the analysis of Oceanographic data, including 'ADCP' measurements, measurements made with 'argo' floats, 'CTD' measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-odbc</td>
<td>1.1.2</td>
<td>A DBI-compatible interface to ODBC databases.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-openssl</td>
<td>0.9.7</td>
<td>Bindings to OpenSSL libssl and libcrypto, plus custom SSH pubkey parsers. Supports RSA, DSA and EC curves P-256, P-384 and P-521. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public key) encryption or EC for Diffie Hellman. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and 'bignum' math methods for manually performing crypto calculations on large multibyte integers.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-packrat</td>
<td>0.4.8_1</td>
<td>Manage the R packages your project depends on in an isolated, portable, and reproducible way.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-pbdzmq</td>
<td>0.2_6</td>
<td>'ZeroMQ' is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize 'ZeroMQ'. We mainly focus on interactive client/server programming frameworks. For convenience, a minimal 'ZeroMQ' library (4.1.0 rc1) is shipped with 'pbdZMQ', which can be used if no system installation of 'ZeroMQ' is available. A few wrapper functions compatible with 'rzmq' are also provided.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-pbkrtest</td>
<td>0.4_7</td>
<td>Test in mixed effects models. Attention is on mixed effects models as implemented in the 'lme4' package. This package implements a parametric bootstrap test and a Kenward Roger modification of F-tests for linear mixed effects models and a parametric bootstrap test for generalized linear mixed models.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-pcapp</td>
<td>1.9_72</td>
<td>Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-perm</td>
<td>1.0_0.0</td>
<td>Perform Exact or Asymptotic permutation tests</td>
<td>GPL</td>
</tr>
<tr>
<td>r-pkgconfig</td>
<td>2.0.1</td>
<td>Set configuration options on a per-package basis. Options set by a given package only apply to that package, other packages are unaffected.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-pkgmaker</td>
<td>0.22</td>
<td>This package provides some low-level utilities to use for package development. It currently provides managers for multiple package specific options and registries, vignette, unit test and bibtex related utilities. It serves as a base package for packages like NMF, RcppOctave, doRNG, and as an incubator package for other general purposes utilities, that will eventually be packaged separately. It is still under heavy development and changes in the interface(s) are more than likely to happen.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-pki</td>
<td>0.1_5.1</td>
<td>PKI functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-plm</td>
<td>1.6_5</td>
<td>A set of estimators and tests for panel data.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-plogr</td>
<td>0.1_1</td>
<td> A simple header-only logging library for C++. Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-plyr</td>
<td>1.8.4</td>
<td>A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-png</td>
<td>0.1_7</td>
<td>This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-polspline</td>
<td>1.1.12</td>
<td>Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-praise</td>
<td>1.0.0</td>
<td>Build friendly R packages that praise their users if they have done something good, or they just need it to feel better.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-proc</td>
<td>1.10.0</td>
<td>Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-processx</td>
<td>2.0.0.1</td>
<td>Portable tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes and their children; restart processes. It can read the standard output and error of the processes, using non-blocking connections. 'processx' can poll a process for standard output or error, with a timeout. It can also poll several processes at once.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-prodlim</td>
<td>1.6.1</td>
<td>Fast and user friendly implementation of nonparametric estimators for censored event history (survival) analysis. Kaplan-Meier and Aalen-Johansen method.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-profilemodel</td>
<td>0.5_9</td>
<td>profileModel provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-profvis</td>
<td>0.3.3</td>
<td>Interactive visualizations for profiling R code.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-proto</td>
<td>1.0.0</td>
<td>An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-pryr</td>
<td>0.1.2</td>
<td>Useful tools to pry back the covers of R and understand the language at a deeper level.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-pspline</td>
<td>1.0_18</td>
<td>Smoothing splines with penalties on order m derivatives.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-psych</td>
<td>1.7.8</td>
<td>A general purpose toolbox for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <http://personality-project.org/r> web page.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-purrr</td>
<td>0.2.3</td>
<td>A complete and consistent functional programming toolkit for R.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-quadprog</td>
<td>1.5_5</td>
<td>This package contains routines and documentation for solving quadratic programming problems.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-quantmod</td>
<td>0.4_11</td>
<td>Specify, build, trade, and analyse quantitative financial trading strategies.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-quantreg</td>
<td>5.33</td>
<td>Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-qvcalc</td>
<td>0.9_1</td>
<td>Functions to compute quasi variances and associated measures of approximation error.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-r.methodss3</td>
<td>1.7.1</td>
<td>Methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4. This is a cross-platform package implemented in pure R that generates standard S3 methods.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-r.oo</td>
<td>1.21.0</td>
<td>Methods and classes for object-oriented programming in R with or without references. Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers. The package has been developed since 2001 and is now considered very stable. This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-r.utils</td>
<td>2.5.0</td>
<td>Utility functions useful when programming and developing R packages.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-r6</td>
<td>2.2.2</td>
<td>The R6 package allows the creation of classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-randomforest</td>
<td>4.6_12</td>
<td>Classification and regression based on a forest of trees using random inputs.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rappdirs</td>
<td>0.3.1</td>
<td>An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' (\url{https://github.com/ActiveState/appdirs}) to R.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-raster</td>
<td>2.5_8</td>
<td>Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rbokeh</td>
<td>0.5.0</td>
<td>A native R plotting library that provides a flexible declarative interface for creating interactive web-based graphics, backed by the Bokeh visualization library <http://bokeh.pydata.org/>.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-rcolorbrewer</td>
<td>1.1_2</td>
<td>Provides color schemes for maps (and other graphics) designed by Cynthia Brewer as described at http://colorbrewer2.org</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-rcpp</td>
<td>0.12.13</td>
<td>The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), the book by Eddelbuettel (2013, Springer) and the paper by Eddelbuettel and Balamuta (2017, PeerJ); see 'citation("Rcpp")' for details.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rcpparmadillo</td>
<td>0.8.100.1.0</td>
<td>'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required. </td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rcppeigen</td>
<td>0.3.3.3.0</td>
<td>R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library (currently version 3.3.3). Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rcpproll</td>
<td>0.2.2</td>
<td>Provides fast and efficient routines for common rolling / windowed operations. Routines for the efficient computation of windowed mean, median, sum, product, minimum, maximum, standard deviation and variance are provided.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rcurl</td>
<td>1.95_4.8</td>
<td>A wrapper for 'libcurl' <http://curl.haxx.se/libcurl/> Provides functions to allow one to compose general HTTP requests and provides convenient functions to fetch URIs, get & post forms, etc. and process the results returned by the Web server. This provides a great deal of control over the HTTP/FTP/... connection and the form of the request while providing a higher-level interface than is available just using R socket connections. Additionally, the underlying implementation is robust and extensive, supporting FTP/FTPS/TFTP (uploads and downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies, redirects, authentication, etc.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-readr</td>
<td>1.1.1</td>
<td>The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-readxl</td>
<td>1.0.0</td>
<td>Import excel files into R. Supports '.xls' via the embedded 'libxls' C library <https://sourceforge.net/projects/libxls/> and '.xlsx' via the embedded 'RapidXML' C++ library <https://rapidxml.sourceforge.net>. Works on Windows, Mac and Linux without external dependencies.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-recipes</td>
<td>0.1.0</td>
<td>An extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models. </td>
<td>GPL2</td>
</tr>
<tr>
<td>r-recommended</td>
<td>3.4.2</td>
<td>R is a free software environment for statistical computing and graphics.</td>
<td>GPL-3.0</td>
</tr>
<tr>
<td>r-registry</td>
<td>0.3</td>
<td>Provides a generic infrastructure for creating and using registries.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-rematch</td>
<td>1.0.1</td>
<td>A small wrapper on 'regexpr' to extract the matches and captured groups from the match of a regular expression to a character vector.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-repr</td>
<td>0.12.0</td>
<td>String and binary representations of objects for several formats / mime types.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-reshape</td>
<td>0.8.7</td>
<td>Flexibly restructure and aggregate data using just two functions: melt and cast.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-reshape2</td>
<td>1.4.2</td>
<td>Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').</td>
<td>MIT</td>
</tr>
<tr>
<td>r-reticulate</td>
<td>1.2</td>
<td>R interface to Python modules, classes, and functions. When calling into Python R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types. Compatible with all versions of Python >= 2.7.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-rex</td>
<td>1.1.1</td>
<td>A friendly interface for the construction of regular expressions.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-rgexf</td>
<td>0.15.3</td>
<td>Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). Using the XML package, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through "sigmajs" (a javascript library) and interact with the igraph package.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rgl</td>
<td>0.98.1</td>
<td>Provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). Output may be on screen using OpenGL, or to various standard 3D file formats including WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-rhive</td>
<td>2.0_0.10</td>
<td>RHive is an R extension facilitating distributed computing via HIVE query. It provides an easy to use HQL like SQL and R objects and functions in HQL.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-rjava</td>
<td>0.9_9</td>
<td>Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-rjdbc</td>
<td>0.2_5</td>
<td>RJDBC is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-rjson</td>
<td>0.2.15</td>
<td>Converts R object into JSON objects and vice-versa</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-rjsonio</td>
<td>1.3_0</td>
<td>This is a package that allows conversion to and from data in Javascript object notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON and was not extensible. rjson's performance is now similar to this package, and perhaps slightly faster in some cases. This package uses methods and is readily extensible by defining methods for different classes, vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional options to allow customizing the generation and processing of JSON content. This package uses libjson rather than implementing yet another JSON parser. The aim is to support other general projects by building on their work, providing feedback and benefit from their ongoing development.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-rlang</td>
<td>0.1.2</td>
<td>A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rlist</td>
<td>0.4.6.1</td>
<td>Provides a set of functions for data manipulation with list objects, including mapping, filtering, grouping, sorting, updating, searching, and other useful functions. Most functions are designed to be pipeline friendly so that data processing with lists can be chained.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-rmarkdown</td>
<td>1.6</td>
<td>Convert R Markdown documents into a variety of formats.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rmr2</td>
<td>3.3.1</td>
<td>Supports the map reduce programming model on top of hadoop streaming</td>
<td>Apache</td>
</tr>
<tr>
<td>r-rms</td>
<td>5.1_1</td>
<td>Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rngtools</td>
<td>1.2.4</td>
<td>This package contains a set of functions for working with Random Number Generators (RNGs). In particular, it defines a generic S4 framework for getting/setting the current RNG, or RNG data that are embedded into objects for reproducibility. Notably, convenient default methods greatly facilitate the way current RNG settings can be changed.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-robustbase</td>
<td>0.92_7</td>
<td>"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rocr</td>
<td>1.0_7</td>
<td>ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rodbc</td>
<td>1.3_15</td>
<td>An ODBC database interface.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rook</td>
<td>1.1_1</td>
<td>This package contains the Rook specification and convenience software for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-roxygen2</td>
<td>6.0.1</td>
<td>Generate your Rd documentation, 'NAMESPACE' file, and collation field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation up-to-date as your requirements change. 'Roxygen2' is inspired by the 'Doxygen' system for C++.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rpart</td>
<td>4.1_11</td>
<td>Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rprojroot</td>
<td>1.2</td>
<td>Robust, reliable and flexible paths to files below a project root. The 'root' of a project is defined as a directory that matches a certain criterion, e.g., it contains a certain regular file.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rsconnect</td>
<td>0.8.5</td>
<td>Programmatic deployment interface for 'RPubs', 'shinyapps.io', and 'RStudio Connect'. Supported content types include R Markdown documents, Shiny applications, Plumber APIs, plots, and static web content.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-rserve</td>
<td>1.7_3</td>
<td>Rserve acts as a socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C++ and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-rsqlite</td>
<td>2.0</td>
<td>Embeds the 'SQLite' database engine in R and provides an interface compliant with the 'DBI' package. The source for the 'SQLite' engine (version 3.8.8.2) is included.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-rstan</td>
<td>2.16.2</td>
<td>User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rstudioapi</td>
<td>0.7</td>
<td>Access the RStudio API (if available) and provide informative error messages when it's not.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-rversions</td>
<td>1.0.3</td>
<td>Query the main 'R' 'SVN' repository to find the versions 'r-release' and 'r-oldrel' refer to, and also all previous 'R' versions and their release dates.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-rvest</td>
<td>0.3.2</td>
<td>Wrappers around the 'xml2' and 'httr' packages to make it easy to download, then manipulate, HTML and XML.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-rzmq</td>
<td>0.9.3</td>
<td>Interface to the 'ZeroMQ' lightweight messaging kernel (see <http://www.zeromq.org/> for more information).</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-sandwich</td>
<td>2.4_0</td>
<td>Model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-scales</td>
<td>0.5.0</td>
<td>Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-seacarb</td>
<td>3.2.2</td>
<td>Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-selectr</td>
<td>0.3_1</td>
<td>Translates a CSS3 selector into an equivalent XPath expression. This allows us to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package 'cssselect' (<https://pythonhosted.org/cssselect/>).</td>
<td>BSD</td>
</tr>
<tr>
<td>r-sf</td>
<td>0.5_4</td>
<td>Support for simple features, a standardized way to encode spatial vector data. Binds to GDAL for reading and writing data, to GEOS for geometrical operations, and to Proj.4 for projection conversions and datum transformations.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-sfsmisc</td>
<td>1.1_1</td>
<td>Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, quite a few related to graphics; some were ported from S-plus.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-shiny</td>
<td>1.0.5</td>
<td>Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-shinybs</td>
<td>0.61</td>
<td>Adds additional Twitter Bootstrap components to Shiny. </td>
<td>GPL3</td>
</tr>
<tr>
<td>r-shinycssloaders</td>
<td>0.2.0</td>
<td>Create a lightweight Shiny wrapper for the css-loaders created by Luke Hass <https://github.com/lukehaas/css-loaders>. Wrapping a Shiny output will automatically show a loader when the output is (re)calculating.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-shinydashboard</td>
<td>0.6.1</td>
<td>Create dashboards with 'Shiny'. This package provides a theme on top of 'Shiny', making it easy to create attractive dashboards.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-shinyjs</td>
<td>0.9.1</td>
<td>Perform common useful JavaScript operations in Shiny apps that will greatly improve your apps without having to know any JavaScript. Examples include: hiding an element, disabling an input, resetting an input back to its original value, delaying code execution by a few seconds, and many more useful functions for both the end user and the developer. 'shinyjs' can also be used to easily call your own custom JavaScript functions from R.</td>
<td>AGPL</td>
</tr>
<tr>
<td>r-shinysky</td>
<td>2.0.0</td>
<td>A set of Shiny UI components includings alerts and styled buttons</td>
<td>MIT</td>
</tr>
<tr>
<td>r-shinythemes</td>
<td>1.1.1</td>
<td>Themes for use with Shiny. Includes several Bootstrap themes from <http://bootswatch.com/>, which are packaged for use with Shiny applications.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-slam</td>
<td>0.1_40</td>
<td>Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-snowballc</td>
<td>0.5.1</td>
<td>An R interface to the C libstemmer library that implements Porter's word stemming algorithm for collapsing words to a common root to aid comparison of vocabulary. Currently supported languages are Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish and Turkish.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-sourcetools</td>
<td>0.1.6</td>
<td>Tools for the reading and tokenization of R code. The 'sourcetools' package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-sp</td>
<td>1.2_5</td>
<td>Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-sparklyr</td>
<td>0.6.3</td>
<td>R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-sparsem</td>
<td>1.77</td>
<td>Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-spatial</td>
<td>7.3_11</td>
<td>Functions for kriging and point pattern analysis.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-stabledist</td>
<td>0.7_1</td>
<td>Density, Probability and Quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-stanheaders</td>
<td>2.16.0_1</td>
<td>The C++ header files of the Stan project are provided by this package, but it contains no R code, vignettes, or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-stringdist</td>
<td>0.9.4.6</td>
<td>Implements an approximate string matching version of R's native 'match' function. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-stringi</td>
<td>1.1.5</td>
<td>Allows for fast, correct, consistent, portable, as well as convenient character string/text processing in every locale and any native encoding. Owing to the use of the ICU library, the package provides R users with platform-independent functions known to Java, Perl, Python, PHP, and Ruby programmers. Available features include: pattern searching (e.g., with ICU Java-like regular expressions or the Unicode Collation Algorithm), random string generation, case mapping, string transliteration, concatenation, Unicode normalization, date-time formatting and parsing, etc.</td>
<td>OTHER</td>
</tr>
<tr>
<td>r-stringr</td>
<td>1.2.0</td>
<td>A consistent, simple and easy to use set of wrappers around the fantastic 'stringi' package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-strucchange</td>
<td>1.5_1</td>
<td>Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-survival</td>
<td>2.41_3</td>
<td>Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.</td>
<td>LGPL</td>
</tr>
<tr>
<td>r-tensorflow</td>
<td>1.4</td>
<td>Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.</td>
<td>APACHE</td>
</tr>
<tr>
<td>r-testit</td>
<td>0.7</td>
<td>Provides two convenience functions assert() and test_pkg() to facilitate testing R packages.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-testthat</td>
<td>1.0.2</td>
<td>A unit testing system designed to be fun, flexible and easy to set up.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-tfruns</td>
<td>1.0</td>
<td>Create and manage unique directories for each 'TensorFlow' training run. Provides a unique, time stamped directory for each run along with functions to retrieve the directory of the latest run or latest several runs. </td>
<td>APACHE</td>
</tr>
<tr>
<td>r-th.data</td>
<td>1.0_8</td>
<td>Contains data sets used in other packages Torsten Hothorn maintains.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-threejs</td>
<td>0.3.1</td>
<td>Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (<https://threejs.org>).</td>
<td>MIT</td>
</tr>
<tr>
<td>r-tibble</td>
<td>1.3.4</td>
<td>Provides a 'tbl_df' class (the 'tibble') that provides stricter checking and better formatting than the traditional data frame.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-tidyr</td>
<td>0.7.1</td>
<td>An evolution of 'reshape2'. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with 'dplyr' data pipelines.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-tidyselect</td>
<td>0.2.2</td>
<td>A backend for the selecting functions of the 'tidyverse'. It makes it easy to implement select-like functions in your own packages in a way that is consistent with other 'tidyverse' interfaces for selection.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-tidytext</td>
<td>0.1.4</td>
<td>Text mining for word processing and sentiment analysis using 'dplyr', 'ggplot2', and other tidy tools.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-tidyverse</td>
<td>1.1.1</td>
<td>The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at <https://github.com/hadley/tidyverse>.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-tilegramsr</td>
<td>0.2.0</td>
<td>R spatial objects for Tilegrams. Tilegrams are tiled maps where the region size is proportional to the certain characteristics of the dataset.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-timedate</td>
<td>3012.100</td>
<td>Environment for teaching "Financial Engineering and Computational Finance". Managing chronological and calendar objects.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-tm</td>
<td>0.7_1</td>
<td>A framework for text mining applications within R.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-tokenizers</td>
<td>0.1.4</td>
<td>Convert natural language text into tokens. The tokenizers have a consistent interface and are compatible with Unicode, thanks to being built on the 'stringi' package. Includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, lines, and regular expressions.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-tseries</td>
<td>0.10_42</td>
<td>Time series analysis and computational finance.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-ttr</td>
<td>0.23_2</td>
<td>Functions and data to construct technical trading rules with R.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-udunits2</td>
<td>0.13</td>
<td>Provides simple bindings to Unidata's udunits library.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-units</td>
<td>0.4_6</td>
<td>Support for measurement units in R vectors, matrices and arrays: automatic propagation, conversion, derivation and simplification of units; raising errors in case of unit incompatibility. Compatible with the POSIXct, Date and difftime classes. Uses the UNIDATA udunits library and unit database for unit compatibility checking and conversion.</td>
<td>GPL2</td>
</tr>
<tr>
<td>r-urca</td>
<td>1.3_0</td>
<td>Unit root and cointegration tests encountered in applied econometric analysis are implemented.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-uuid</td>
<td>0.1_2</td>
<td>Tools for generating and handling of UUIDs (Universally Unique Identifiers).</td>
<td>MIT</td>
</tr>
<tr>
<td>r-vars</td>
<td>1.5_2</td>
<td>Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-vgam</td>
<td>1.0_4</td>
<td>An implementation of about 6 major classes of statistical regression models. At the heart of it are the vector generalized linear and additive model (VGLM/VGAM) classes, and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and VGAM package. Currently only fixed-effects models are implemented, i.e., no random-effects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs (i.e., with smoothing). The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-viridis</td>
<td>0.4.0</td>
<td>Port of the new 'matplotlib' color maps ('viridis' - the default -, 'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/ > is a popular plotting library for 'python'. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-viridislite</td>
<td>0.2.0</td>
<td>Port of the new 'matplotlib' color maps ('viridis' - the default -, 'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/ > is a popular plotting library for 'python'. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness. This is the 'lite' version of the more complete 'viridis' package that can be found at <https://cran.r-project.org/package=viridis>.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-visnetwork</td>
<td>2.0.1</td>
<td>Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks.</td>
<td>MIT</td>
</tr>
<tr>
<td>r-weatherdata</td>
<td>0.5.0</td>
<td>Functions that help in fetching weather data from websites. Given a location and a date range, these functions help fetch weather data (temperature, pressure etc.) for any weather related analysis.</td>
<td>GPL</td>
</tr>
<tr>
<td>r-whisker</td>
<td>0.3_2</td>
<td>logicless templating, reuse templates in many programming languages including R</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-withr</td>
<td>2.0.0</td>
<td>A set of functions to run code 'with' safely and temporarily modified global state. Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-xlsx</td>
<td>0.5.7</td>
<td>Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-xlsxjars</td>
<td>0.6.1</td>
<td>The xlsxjars package collects all the external jars required for the xlxs package. This release corresponds to POI 3.10.1.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-xml</td>
<td>3.98_1.9</td>
<td>Many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP. Also offers access to an 'XPath' "interpreter".</td>
<td>BSD</td>
</tr>
<tr>
<td>r-xml2</td>
<td>1.1.1</td>
<td>Work with XML files using a simple, consistent interface. Built on top of the 'libxml2' C library.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-xtable</td>
<td>1.8_2</td>
<td>Coerce data to LaTeX and HTML tables.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-xts</td>
<td>0.10_0</td>
<td>Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.</td>
<td>GPL3</td>
</tr>
<tr>
<td>r-yaml</td>
<td>2.1.14</td>
<td>Implements the 'libyaml' 'YAML' 1.1 parser and emitter (<http://pyyaml.org/wiki/LibYAML>) for R.</td>
<td>BSD</td>
</tr>
<tr>
<td>r-zoo</td>
<td>1.8_0</td>
<td>An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.</td>
<td>GPL3</td>
</tr>
<tr>
<td>rpy2</td>
<td>2.8.6</td>
<td>Python interface to the R language (embedded R)</td>
<td>GPL-2.0</td>
</tr>
<tr>
<td>rpy2</td>
<td>2.9.0</td>
<td>Python interface to the R language (embedded R)</td>
<td>GPL-2.0</td>
</tr>
<tr>
<td>rstudio</td>
<td>1.1.383</td>
<td>A set of integrated tools designed to help you be more productive with R</td>
<td>GPL</td>
</tr>
</table>
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