R_packages.R

x <- c(
	'knitr', #  A general-purpose package for dynamic report generation in R.
# 'sqldf', # For running SQL statements on R data frames, optimized for convenience.
	'randomForest', # Classification and regression based on a forest of trees using random inputs.
	'arm', # R functions for processing lm, glm, svy.glm, mer and polr outputs.
	'ggplot2', # An implementation of the Grammar of Graphics.
	'gridExtra', # misc. high-level Grid functions
	'plyr', # Tools for splitting, applying and combining data.
	'tree', # Classification and regression trees.
	'gbm', # Generalized Boosted Regression Models
	'XML', # Tools for parsing and generating XML
	'foreign', # Functions for reading and writing data stored by statistical packages such as Minitab, S, SAS, SPSS, Stata, Systat, ..., and for reading and writing dBase files.
	'xtable', # Export tables to LaTeX or HTML
	'lubridate', # makes it easier to work with dates and times by providing functions to identify and parse date-time data
	'stringr', # Make it easier to work with strings
	'tm', # A framework for text mining applications within R.
	'lda', # Collapsed Gibbs sampling methods for topic models. This package implements latent Dirichlet allocation (LDA) and related models.
	'reshape2', # Reshape lets you flexibly restructure and aggregate data using just two functions: melt and cast.
	'lme4', # Linear mixed-effects models using S4 classes.
	'coda', # Output analysis and diagnostics for Markov Chain Monte Carlo simulations.
	'mvtnorm', # Multivariate Normal and t Distributions.
	'ellipse', # Functions for drawing ellipses and ellipse-like confidence regions.
	'rjson', # Converts R object into JSON objects and vice-versa.
	'sde', # Simulation and Inference for Stochastic Differential Equations
	'RCurl', # General network (HTTP/FTP/...) client interface for R
#	'sendmailR', # send email using R
	'twitteR', # R based Twitter client
	'animation', # A gallery of animations in statistics and utilities to create animations.
#	'ProjectTemplate', # Automates the creation of new statistical analysis projects.
	'rjags', # Interface to the JAGS MCMC library.
	'doMC', # for multi-core processing
	'mcmcplots', # for visual diagnostics of posterior samples
	'shiny' # elegant and powerful web framework for building interactive web applications using R
)
install.packages(x)
rm(x)

## RStan:
# More info @ https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started#how-to-install-rstan
# Prereqs:
install.packages('inline')
install.packages('Rcpp')#,type="source")
# If previous version of RStan is installed:
library(rstan)
set_cppo('fast')
detach("package:rstan", unload = TRUE)
remove.packages('rstan') 
# Actual installation:
options(repos = c(getOption("repos"), rstan = "http://wiki.rstan-repo.googlecode.com/git/"))
install.packages('rstan', type = 'source')
# Test:
library(rstan)
set_cppo("fast")  # for best running speed