factorial: 階乗 (引数は非負整数)
gamma: ガンマ関数 (階乗の拡張. 引数 n が 1以上の整数ならば, n - 1 の階乗に等しい)
sum: 総和
sum-of-squares: 平方の総和
prod: 総積
diag: 与えられたシーケンスを対角成分とする行列
rank: 実行数値ランク(非負数) Returns the effective numerical matrix rank, which is the number of nonnegligible singular values.
det: 行列式の値
trans: 転置行列
trace: トレース (正方行列の対角成分の和)
nrow: 行の個数
ncol: 列の個数
dim: 次元 (与えられた行列の行および列の個数からなるベクタで返す)
diag: 対角成分 (シーケンスで返す)
各要素に演算を施す
abs: 絶対値
sq: 平方
sqrt: 平方根
exp: 指数関数 (ネイピア数の冪乗)
log: 自然対数
log2: 底 2 の対数
log10: 常用対数
sin: 正弦
cos: 余弦
tan: 正接
asin: 逆正弦
acos: 逆余弦
atan: 逆正接
各要素同士で以下の演算を行う
引数のどちらかが数ならば, 全ての要素がその数であり, もう一方の引数と同型の引数を与えた場合と同じ
atan2: 逆正接 (引数二つのそれぞれの要素を y x として, (引数一つの場合は元の要素のまま))
pow: 冪乗
inner-product: 内積 (引数を m n とすると m * n^T がシーケンスになる場合に限る)
引数のいずれかが数ならば, 全ての要素がその数であり, 他の引数と同型の引数を与えた場合と同じ
plus: 加算
minus: 減算
mult: 乗算
div: 除算
mmult: 乗算 (乗算可能なサイズで並んでいなければならない)
$: An alias to (sel (second args) :cols (first args)). If given only a single argument,
$=: Formula macro translates from infix to prefix
$data: This variable is bound to a dataset when the with-data macro is used.
$fn: A simple macro used as syntactic sugar for defining predicate functions to be used
$group-by: Returns a map of datasets keyed by a query-map corresponding the group.
$join: Returns a dataset created by right-joining two datasets.
$map: This function returns a sequence resulting from mapping the given function over
$order: Sorts a dataset by the given columns in either ascending (:asc)
$rollup: Returns a dataset that uses the given summary function (or function identifier keyword)
$where: An alias to (query-dataset (second args) (first args)). If given only a single argument,
->Dataset: Positional factory function for class incanter.core.Dataset.
allowed-types: NO DOCSTRING
beta: Equivalent to R's beta function.
bind-columns: Returns the matrix resulting from concatenating the given matrices
bind-rows: Returns the matrix resulting from concatenating the given matrices
categorical-var: Returns a categorical variable based on the values in the given collection.
choose: Returns number of k-combinations (each of size k) from a set S with
col-names: If given a dataset, it returns its column names. If given a dataset and a sequence
condition: Returns the two norm condition number, which is max(S) / min(S), where S is the diagonal matrix of singular values from an SVD decomposition.
conj-cols: Returns a dataset created by merging the given datasets and/or collections.
conj-rows: Returns a dataset created by combining the rows of the given datasets and/or collections.
copy: Returns a copy of the given matrix.
count-col-types: Takes in a column name or number and a dataset. Returns a raw count of each type present in that column. Counts nils.
count-types: Helper function. Takes in a seq (usually from a column from an Incanter dataset) and returns a map of types -> counts of the occurance
cumulative-sum: Returns a sequence of cumulative sum for the given collection. For instance
data-table: Creates a javax.swing.JTable given an Incanter dataset.
dataset: Returns a map of type incanter.core.dataset constructed from the given column-names and
dataset?: Determines if obj is of type incanter.core.Dataset.
decomp-cholesky: Returns the Cholesky decomposition of the given matrix. Equivalent to R's
decomp-eigenvalue: Returns the Eigenvalue Decomposition of the given matrix. Equivalent to R's eig function.
decomp-lu: Returns the LU decomposition of the given matrix.
decomp-qr: Returns the QR decomposition of the given matrix. Equivalent to R's qr function.
decomp-svd: Returns the Singular Value Decomposition (SVD) of the given matrix. Equivalent to
deshape: Returns a dataset where the columns identified by :merge are collapsed into
except-for: Returns a lazy list of numbers ranging from 0 to n, except for the given exceptions.
except-for-cols:
get-categories: Given a dataset and one or more column keys, returns the set of categories for them.
get-column-id: NO DOCSTRING
get-columns: NO DOCSTRING
get-dummies: NO DOCSTRING
grid-apply: Applies the given function f, that accepts two arguments, to a grid
group-on: Groups the given matrix by the values in the columns indicated by the
half-vectorize: Returns the half-vectorization (i.e. vech) of the given matrix.
head: Returns the head of the dataset. 10 or full dataset by default.
identity-matrix: Returns an n-by-n identity matrix.
incomplete-beta: Returns the non-regularized incomplete beta value.
inner-product: NO DOCSTRING
kronecker: Returns the Kronecker product of the given arguments.
length: A version of count that works on collections, matrices, and numbers.
make-unique: Take a sequence of keywords and make them unique by possibly
map->Dataset: Factory function for class incanter.core.Dataset, taking a map of keywords to field values.
map-get: NO DOCSTRING
matrix: Returns an instance of an incanter.Matrix, which is an extension of
matrix-map: Like clojure.core/map, but will work on matrices of any dimension:
matrix?: Test if obj is 'derived' clatrix.core.Matrix
proj: NO DOCSTRING
query-dataset: Queries the given dataset using the query-map, returning a new dataset.
query-to-pred: Given a query-map, it returns a function that accepts a hash-map and returns true if it
quit: Exits the Clojure shell.
rank: Returns the effective numerical matrix rank, which is the number of nonnegligible singular values.
regularized-beta: Returns the regularized incomplete beta value. Equivalent to R's pbeta function.
reorder-columns: Produce a new dataset with the columns in the specified order.
safe-div: DivideByZero safe alternative to clojures / function,
save: Save is a multi-function that is used to write matrices, datasets and
sel: Returns an element or subset of the given matrix, dataset, or list.
set-data: Examples:
solve: Returns a matrix solution if A is square, least squares solution otherwise.
solve-quadratic: Returns a vector with the solution to x from the quadratic
string-to-categorical: NO DOCSTRING
submap: NO DOCSTRING
symmetric-matrix: Returns a symmetric matrix from the given data, which represents the lower triangular elements
tail: Returns the tail of the dataset. 10 or full dataset by default.
to-dataset: Returns a dataset containing the given values.
to-dummies: NO DOCSTRING
to-labels:
to-levels:
to-list: Returns a list-of-lists if the given matrix is two-dimensional
to-map: Takes a dataset or matrix and returns a hash-map where the keys are
to-matrix: Converts a dataset into a matrix. Equivalent to R's as.matrix function
to-vect: Returns a vector-of-vectors if the given matrix is two-dimensional
toeplitz: Returns the Toeplitz matrix for the given vector, which form the first row of the matrix
transform-col: Apply function f & args to the specified column of dataset and replace the column
vector-length: NO DOCSTRING
vectorize: Returns the vectorization (i.e. vec) of the given matrix.
view: This is a general 'view' function. When given an Incanter matrix/dataset
with-data: Binds the given data to $data and executes the body.
mean: 平均値
variance: 分散 (平均値との差の二乗の総和を要素数で除算)
test-statistic-iterations: NO DOCSTRING
test-statistic-map: NO DOCSTRING
->Beta-rec: Positional factory function for class incanter.distributions.Beta-rec.
->Binomial-rec: Positional factory function for class incanter.distributions.Binomial-rec.
->ChiSquare-rec: Positional factory function for class incanter.distributions.ChiSquare-rec.
->Combination: Positional factory function for class incanter.distributions.Combination.
->DoubleUniform-rec: Positional factory function for class incanter.distributions.DoubleUniform-rec.
->Exponential-rec: Positional factory function for class incanter.distributions.Exponential-rec.
->F: Positional factory function for class incanter.distributions.F.
->Gamma-rec: Positional factory function for class incanter.distributions.Gamma-rec.
->NegativeBinomial-rec: Positional factory function for class incanter.distributions.NegativeBinomial-rec.
->Normal-rec: Positional factory function for class incanter.distributions.Normal-rec.
->Poisson-rec: Positional factory function for class incanter.distributions.Poisson-rec.
->StudentT-rec: Positional factory function for class incanter.distributions.StudentT-rec.
->UniformInt: Positional factory function for class incanter.distributions.UniformInt.
Distribution: The distribution protocol defines operations on probability distributions.
beta-distribution: Returns a Beta distribution that implements the incanter.distributions.Distribution protocol.
binomial-distribution: Returns a Binomial distribution that implements the incanter.distributions.Distribution protocol.
cdf: A function of the incanter.distribution.Distribution protocol.
chisq-distribution: Returns a Chi-square distribution that implements the incanter.distributions.Distribution protocol.
colt-extenders: NO DOCSTRING
combination-distribution: Create a distribution of all the k-sized combinations of n integers.
decode-combinadic: Decodes a 0 to nCk - 1 integer into its combinadic form, a set of
draw: A function of the incanter.distribution.Distribution protocol.
exponential-distribution: Returns a Exponential distribution that implements the incanter.distributions.Distribution protocol.
f-distribution: Returns a F-distribution that implements the incanter.distributions.Distribution protocol.
gamma-distribution: Returns a Gamma distribution that implements the incanter.distributions.Distribution protocol.
inf+: NO DOCSTRING
inf-: NO DOCSTRING
integer-distribution: Create a uniform distribution over a set of integers over
map->Beta-rec: Factory function for class incanter.distributions.Beta-rec, taking a map of keywords to field values.
map->Binomial-rec: Factory function for class incanter.distributions.Binomial-rec, taking a map of keywords to field values.
map->ChiSquare-rec: Factory function for class incanter.distributions.ChiSquare-rec, taking a map of keywords to field values.
map->Combination: Factory function for class incanter.distributions.Combination, taking a map of keywords to field values.
map->DoubleUniform-rec: Factory function for class incanter.distributions.DoubleUniform-rec, taking a map of keywords to field values.
map->Exponential-rec: Factory function for class incanter.distributions.Exponential-rec, taking a map of keywords to field values.
map->F: Factory function for class incanter.distributions.F, taking a map of keywords to field values.
map->Gamma-rec: Factory function for class incanter.distributions.Gamma-rec, taking a map of keywords to field values.
map->NegativeBinomial-rec: Factory function for class incanter.distributions.NegativeBinomial-rec, taking a map of keywords to field values.
map->Normal-rec: Factory function for class incanter.distributions.Normal-rec, taking a map of keywords to field values.
map->Poisson-rec: Factory function for class incanter.distributions.Poisson-rec, taking a map of keywords to field values.
map->StudentT-rec: Factory function for class incanter.distributions.StudentT-rec, taking a map of keywords to field values.
map->UniformInt: Factory function for class incanter.distributions.UniformInt, taking a map of keywords to field values.
nCk: NO DOCSTRING
neg-binomial-distribution: Returns a Negative binomial distribution that implements the incanter.distributions.Distribution protocol.
normal-distribution: Returns a Normal distribution that implements the
pdf: A function of the incanter.distribution.Distribution protocol.
poisson-distribution: Returns a Poisson distribution that implements the incanter.distributions.Distribution protocol.
res-sampler: Get a sample from the nCk possible combinations. Uses a reservoir
roulette-wheel: Perform a roulette wheel selection given a list of frequencies
simple-cdf: Compute the CDF at a value by getting the support and adding up the values until
support: **** EXPERIMENTAL ****
t-distribution: Returns a Student-t distribution that implements the incanter.distributions.Distribution protocol.
tabulate: Private tabulation function that works on any data type, not just numerical
take-to-first: Returns a lazy sequence of successive items from coll up to
test-statistic-distribution: Create a distribution of the test-statistic over the possible
uniform-distribution: Returns a Uniform distribution that implements the incanter.distributions.Distribution protocol.
sd: 標準偏差 (試料標準偏差. 平均値との差の二乗の総和を要素数 - 1 で除算したものの平方根) mean: 平均 (算術平均) median: 中央値
sample: 要素の無作為な標本のシーケンス
:size で返すベクタの要素数を指定, デフォルトは与えたコレクションと同じ要素数
:replacement に false を指定すると, 同一要素が二つ以上含まれない標本を返す (:size で, 元のコレクションの要素数より大きな数を指定すると例外)
correlation: 相関係数 (-1.0~1.0 の数)
auto-correlation: Returns the auto-correlation of x with given lag, mean, and variance.
benford-law: NO DOCSTRING
benford-probs: NO DOCSTRING
benford-test: Performs Benford's Law test using chisq-test.
bigrams: NO DOCSTRING
bool-to-binary: NO DOCSTRING
bootstrap: Returns a bootstrap sample of the given statistic on the given data.
category-col-summarizer: Returns a summarizer function which takes a category column and returns a list of the top 5 columns by volume, and a
cdf-beta: Returns the Beta cdf of the given value of x. It will return a sequence
cdf-binomial: Returns the Binomial cdf of the given value of x. It will return a sequence
cdf-chisq: Returns the Chi Square cdf of the given value of x. It will return a sequence
cdf-empirical: Returns a step-function representing the empirical cdf of the given data.
cdf-exp: Returns the Exponential cdf of the given value of x. It will return a sequence
cdf-f: Returns the F-distribution cdf of the given value, x. It will return a sequence
cdf-gamma: Returns the Gamma cdf for the given value of x. It will return a sequence
cdf-neg-binomial: Returns the Negative Binomial cdf of the given value of x. It will return a sequence
cdf-normal: Returns the Normal cdf of the given value, x. It will return a sequence
cdf-poisson: Returns the Poisson cdf of the given value of x. It will return a sequence
cdf-t: Returns the Student's t cdf for the given value of x. It will return a sequence
cdf-uniform: Returns the Uniform cdf of the given value of x. It will return a sequence
cdf-weibull: Returns the Weibull cdf for the given value of x. It will return a sequence
chebyshev-distance: In the limiting case of Lp reaching infinity we obtain the Chebyshev distance.
chisq-test: Performs chi-squared contingency table tests and goodness-of-fit tests.
choose-singletype-col-summarizer: Takes in a type, and returns a suitable column summarizer
concordant?: NO DOCSTRING
correlation: Returns the sample correlation of x and y, or the correlation
correlation-linearity-test: http://en.wikipedia.org/wiki/Correlation_ratio
correlation-ratio: http://en.wikipedia.org/wiki/Correlation_ratio
cosine-similarity: http://en.wikipedia.org/wiki/Cosine_similarity
count-col-types: Takes in a column name or number and a dataset. Returns a raw count of each type present in that column. Counts nils.
covariance: Returns the sample covariance of x and y.
cumulative-mean: Returns a sequence of cumulative means for the given collection. For instance
damerau-levenshtein-distance: NO DOCSTRING
deep-merge-with: Copied here from clojure.contrib.map-utils. The original may have
detabulate: Take a contingency table of counts and returns a matrix of observations.
dice-coefficient: http://en.wikipedia.org/wiki/Dice%27s_coefficient
dice-coefficient-str: http://en.wikipedia.org/wiki/Dice%27s_coefficient
discordant-pairs: http://en.wikipedia.org/wiki/Discordant_pairs
discordant?: NO DOCSTRING
euclidean-distance: http://en.wikipedia.org/wiki/Euclidean_distance
f-test: Test for different variances between 2 samples
first-digit: NO DOCSTRING
gamma-coefficient: http://www.statsdirect.com/help/nonparametric_methods/kend.htm
get-counts: NO DOCSTRING
hamming-distance: http://en.wikipedia.org/wiki/Hamming_distance
indicator: Returns a sequence of ones and zeros, where ones
jaccard-distance: http://en.wikipedia.org/wiki/Jaccard_index
jaccard-index: http://en.wikipedia.org/wiki/Jaccard_index
kendalls-tau: http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient
kendalls-tau-distance: NO DOCSTRING
kendalls-w: http://en.wikipedia.org/wiki/Kendall%27s_W
key-compare: NO DOCSTRING
kurtosis: Returns the kurtosis of the data, x. "Kurtosis is a measure of the "peakedness"
kv-compare: NO DOCSTRING
lee-distance: http://en.wikipedia.org/wiki/Lee_distance
levenshtein-distance: http://en.wikipedia.org/wiki/Levenshtein_distance
linear-model: Returns the results of performing a OLS linear regression of y on x.
mahalanobis-distance: Returns the Mahalanobis distance between x, which is
manhattan-distance: http://en.wikipedia.org/wiki/Manhattan_distance
minkowski-distance: http://en.wikipedia.org/wiki/Minkowski_distance
n-grams: returns a set of the unique n-grams in a string.
normalized-kendall-tau-distance: http://en.wikipedia.org/wiki/Kendall_tau_distance
numeric-col-summarizer: Returns a summarizer function which takes a purely numeric column with no non-numeric values
odds-ratio: http://en.wikipedia.org/wiki/Odds_ratio
pairings: confusing ass name.
pairs: returns unique pairs of a and b where members of a and b can not be paired with the corresponding slot in the other list.
pdf-beta: Returns the Beta pdf of the given value of x. It will return a sequence
pdf-binomial: Returns the Binomial pdf of the given value of x. It will return a sequence
pdf-chisq: Returns the Chi Square pdf of the given value of x. It will return a sequence
pdf-exp: Returns the Exponential pdf of the given value of x. It will return a sequence
pdf-f: Returns the F pdf of the given value, x. It will return a sequence
pdf-gamma: Returns the Gamma pdf for the given value of x. It will return a sequence
pdf-neg-binomial: Returns the Negative Binomial pdf of the given value of x. It will return a sequence
pdf-normal: Returns the Normal pdf of the given value, x. It will return a sequence
pdf-poisson: Returns the Poisson pdf of the given value of x. It will return a sequence
pdf-t: Returns the Student's t pdf for the given value of x. It will return a sequence
pdf-uniform: Returns the Uniform pdf of the given value of x. It will return a sequence
pdf-weibull: Returns the Weibull pdf for the given value of x. It will return a sequence
permute: If provided a single argument, returns a permuted version of the
predict: Takes a linear-model and an x value (either a scalar or vector)
principal-components: Performs a principal components analysis on the given data matrix.
product-marginal-test: the joint PMF of independent variables is equal to the product of their marginal PMFs.
quantile: Returns the quantiles of the data, x. By default it returns the min,
quantile-normal: Returns the inverse of the Normal CDF for the given probability.
quantile-t: Returns the inverse of the Student's t CDF for the given probability
rank-index: given a seq, returns a map where the keys are the values of the seq and the values are the positional rank of each member o the seq.
sample-beta: Returns a sample of the given size from a Beta distribution.
sample-binomial: Returns a sample of the given size from a Binomial distribution.
sample-chisq: Returns a sample of the given size from a Chi Square distribution
sample-dirichlet: Examples:
sample-exp: Returns a sample of the given size from a Exponential distribution.
sample-gamma: Returns a sample of the given size from a Gamma distribution.
sample-inv-wishart: Returns a p-by-p symmetric distribution drawn from an inverse-Wishart distribution
sample-multinomial: Returns a sequence representing a sample from a multinomial distribution.
sample-mvn: Returns a sample of the given size from a Multivariate Normal
sample-neg-binomial: Returns a sample of the given size from a Negative Binomial distribution.
sample-normal: Returns a sample of the given size from a Normal distribution
sample-permutations: If provided a two arguments (n x), it returns a list of n permutations
sample-poisson: Returns a sample of the given size from a Poisson distribution.
sample-t: Returns a sample of the given size from a Student's t distribution.
sample-uniform: Returns a sample of the given size from a Uniform distribution.
sample-weibull: Returns a sample of the given size from a Weibull distribution
sample-wishart: Returns a p-by-p symmetric distribution drawn from a Wishart distribution
scalar-abs: Fast absolute value function
simple-ci: Get the confidence interval for the data.
simple-p-value: Returns the p-value for the data contained in coll.
simple-regression: A stripped version of linear-model that returns a map containing only
simple-t-test: Perform a simple t-test on the data contained in coll.
skewness: Returns the skewness of the data, x. "Skewness is a measure of the asymmetry
sorensen-index: http://en.wikipedia.org/wiki/S%C3%B8rensen_similarity_index#cite_note-4
sort-map: NO DOCSTRING
spearmans-rho: http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
square-devs-from-mean: takes either a sample or a sample and a precalculated mean.
stat-summarizable: Placeholder stub function, for more advanced cases where we want to automatically ignore occasional bad values in a column.
sum-of-square-devs-from-mean: takes either a sample or a sample and a precalculated mean.
sum-variance-test: the variance of the sum of n independent variables is equal to the sum of their variances.
summarizable?: Takes in a column name (or number) and a dataset. Returns true if the column can be summarized, and false otherwise
summarizer-fn: Takes in a column (number or name) and a dataset. Returns a function to summarize the column if summarizable, and a
summary: Takes in a dataset. Returns a summary of that dataset (as a map of maps), having automatically figured out the relevant
sweep: Return an array obtained from an input array by sweeping out a
t-test: Argument:
tabulate: Cross-tabulates the values of the given numeric matrix.
tanimoto-coefficient: http://en.wikipedia.org/wiki/Jaccard_index
tree-comp-each: NO DOCSTRING
variance: Returns the sample variance of the data, x. Equivalent to R's var function.
within: y is within z of x in metric space.
add-box-plot: Adds an additional box to an existing box-plot, returns the modified chart object.
add-box-plot*: NO DOCSTRING
add-categories: Adds an additional categories to an existing bar-chart or line-chart, returns the modified chart object.
add-categories*: NO DOCSTRING
add-function: Adds a xy-plot of the given function to the given chart, returning
add-function*: NO DOCSTRING
add-histogram: Adds a histogram to an existing histogram plot, returns the modified
add-histogram*: NO DOCSTRING
add-image: Adds an image to the chart at the given coordinates.
add-lines: Plots lines on the given scatter or line plot (xy-plot) of the (x,y) points.
add-lines*: NO DOCSTRING
add-parametric: Adds a xy-plot of the given parametric function to the given chart, returning
add-parametric*: NO DOCSTRING
add-pointer: Adds an arrow annotation to the given chart.
add-points: Plots points on the given scatter-plot or xy-plot of the (x,y) points.
add-points*: NO DOCSTRING
add-polygon: Adds a polygon outline defined by a given coordinates. The last coordinate will
add-subtitle: Adds a JFreeChart title object to a chart as a subtitle.
add-text: Adds a text annotation centered at the given coordinates.
area-chart: Returns a JFreeChart object representing an area-chart of the given data.
area-chart*: NO DOCSTRING
bar-chart: Returns a JFreeChart object representing a bar-chart of the given data.
bar-chart*: NO DOCSTRING
bland-altman-plot: Options:
box-plot: Returns a JFreeChart object representing a box-plot of the given data.
box-plot*: NO DOCSTRING
candle-stick-plot: NO DOCSTRING
candle-stick-plot*: NO DOCSTRING
clear-background: Sets the alpha level (transparency) of the plot's background to zero
create-time-series-plot: NO DOCSTRING
create-xy-plot: NO DOCSTRING
create-xy-series-plot: NO DOCSTRING
data-as-list: NO DOCSTRING
dynamic-scatter-plot: Returns an scatter-plot bound to sliders (which tend to appear behind the chart).
dynamic-xy-plot: Returns an xy-plot bound to sliders (which tend to appear behind the chart).
function-plot: Returns a xy-plot object of the given function over the range indicated
function-plot*: NO DOCSTRING
get-series: get-series
heat-map: Usage: (heat-map function x-min x-max y-min y-max & options)
heat-map*: NO DOCSTRING
histogram: Returns a JFreeChart object representing the histogram of the given data.
histogram*: NO DOCSTRING
in-coll: NO DOCSTRING
incanter.charts.proxy$java.lang.Object$ChangeListener$22db79c0: NO DOCSTRING
incanter.charts.proxy$org.jfree.chart.plot.Plot$0: NO DOCSTRING
incanter.charts.proxy$org.jfree.data.xy.AbstractXYDataset$0: NO DOCSTRING
line-chart: Returns a JFreeChart object representing a line-chart of the given values and categories.
line-chart*: NO DOCSTRING
log-axis: Create a logarithmic axis.
parametric-plot: Returns a xy-plot object of the given parametric function over the range indicated
parametric-plot*: NO DOCSTRING
pie-chart: Returns a JFreeChart object representing a pie-chart of the given data.
pie-chart*: NO DOCSTRING
qq-plot: Returns a QQ-Plot object. Use the 'view' function to display it.
range-inclusive: NO DOCSTRING
scatter-plot: Returns a JFreeChart object representing a scatter-plot of the given data.
scatter-plot*: NO DOCSTRING
scatter-plot-matrix: Returns a JFreeChart object displaying a scatter plot matrix for the given data.
scatter-plot-matrix*: NO DOCSTRING
set-alpha: Sets the alpha level (transparency) of the plot's foreground
set-axis: Set the selected axis of the chart, returning the chart.
set-background-alpha: Sets the alpha level (transparency) of the plot's background
set-background-default: Examples:
set-stroke: Examples:
set-stroke-color: Examples:
set-theme: Changes the chart theme.
set-theme-bw: Examples:
set-theme-default: NO DOCSTRING
set-title: Sets the main title of the plot, returns the modified chart object.
set-x-label: Sets the label of the x-axis, returns the modified chart object.
set-x-range: Sets the range of the x-axis on the given chart.
set-y-label: Sets the label of the y-axis, returns the modified chart object.
set-y-range: Sets the range of the y-axis on the given chart.
slider: Examples:
sliders: Creates one slider control for each of the given sequence bindings.
sliders*: sliders*
stacked-area-chart: Returns a JFreeChart object representing an stacked-area-chart of the given data.
stacked-area-chart*: NO DOCSTRING
stacked-bar-chart: Returns a JFreeChart object representing an stacked-bar-chart of the given data.
stacked-bar-chart*: NO DOCSTRING
time-series-plot: Returns a JFreeChart object representing a time series plot of the given data.
time-series-plot*: NO DOCSTRING
trace-plot: Returns a trace-plot object, use the 'view' function to display it.
xy-plot: Returns a JFreeChart object representing a xy-plot of the given data.
xy-plot*: NO DOCSTRING
interpolate: Builds a function that interpolates given collection of points.
interpolate-grid: Interpolates 2-dimensional grid. Returns function f that takes 2 arguments: x and y.
interpolate-grid*: NO DOCSTRING
interpolate-parametric: Builds a parametric function that interpolates given collection of points.
validate-unique: NO DOCSTRING
sample-model-params: Returns a sample of the given size of the the parameters (coefficients and
sample-multinomial-params: Returns a sample of multinomial proportion parameters.
sample-mv-model-params: Examples:
sample-mvn-params: Returns samples of means (sampled from an mvn distribution) and vectorized covariance
sample-proportions: sample-proportions has been renamed sample-multinomial-params
censored-mean-lower: Returns the mean of a normal distribution (with mean mu and standard
censored-mean-two-sided: Returns the mean of a normal distribution (with mean mu and standard
censored-mean-upper: Returns the mean of a normal distribution (with mean mu and standard
censored-variance-lower: Returns the variance of a normal distribution (with mean mu and standard
censored-variance-two-sided: Returns the variance of a normal distribution (with mean mu and standard
censored-variance-upper: Returns the variance of a normal distribution (with mean mu and standard
lambda-lower: NO DOCSTRING
lambda-two-sided: NO DOCSTRING
lambda-upper: NO DOCSTRING
psi: NO DOCSTRING
truncated-variance: Returns the variance of a normal distribution truncated at a and b.
datasets: NO DOCSTRING
datasets-base-url: NO DOCSTRING
get-dataset: Returns the sample dataset associated with the given key. Most datasets
commit-sheet!: Internally save the dataset into the :sheet object.
read-sheet: NO DOCSTRING
read-xls: Read an Excel file into a dataset. Note: cells containing formulas will be
save-xls: Save a dataset to an Excel file. Can save in both older and newer
+highest-precedence+: NO DOCSTRING
+precedence-table+: NO DOCSTRING
+translation-table+: NO DOCSTRING
defop: Define operators for formula macro
find-lowest-precedence: find the operator with lowest precedence; search from left to right
formula: Convert from infix notation to prefix notation
infix-to-prefix: Convert from infix notation to prefix notation
operator?: Check if is valid operator
translate-op: Translation of symbol => symbol for binary op allows for
parse-string: NO DOCSTRING
read-dataset: Returns a dataset read from a file or a URL.
add-latex: Adds an LaTeX equation annotation to the chart at the given x,y coordinates.
add-latex-subtitle: Adds the given LaTeX equation as a subtitle to the chart.
latex: Returns the given LaTeX equation rendered as an java.awt.Image.
to-latex: Convert an Incanter Matrix into a string of LaTeX commands to render it.
fetch-dataset: Queries a MongoDB database, accepting the same arguments as
insert-dataset: Inserts the rows of the Incanter dataset into the given MongoDB collection.
derivative: Returns a function that approximates the derivative of the given function.
dot: Scalar product of a pair of collections
fmin-bfgs: Minimize a function of multiple variables using the BFGS algorthim, based
gradient: Returns a function that calculates a 5-point approximation to
gradient-fn: Returns a function that approximates the gradient of the given function,
gradient-forward-diff: Examples:
hessian: Returns a function that calculates an approximation to the Hessian matrix
hessian-fn: Examples:
integrate: Integrate a function f from a to b
line-search-BFGS: Minimize alpha in the function f(x-k + alpha*p-k) using the interpolation
maximize: This function tries to maximize a scalar function of one or more variables.
minimize: Minimize a scalar function of one or more variables. Based on the
nls-gauss-newton: Examples:
nls-gradient: Returns the gradient of the least squares function applied to the given function evaluated
nls-hessian: Returns the Hessian matrix of the least squares function applied to f evaluated
nls-newton-raphson: Examples:
nls-rss: Returns the residual sum-of-squares for the given function evaluated at x with
non-linear-model: Determine the nonlinear least-squares estimates of the
partial-derivative: Examples:
second-partial-derivative: Examples:
with-counting: Takes a function and returns a version of the function that tracks how many
save-pdf: Save a chart object as a pdf document.
alpha-fn: NO DOCSTRING
beta-fn: NO DOCSTRING
dist-euclidean: NO DOCSTRING
get-distances: NO DOCSTRING
get-min-dist: NO DOCSTRING
som-batch-train: Performs BL-SOM (batch-learning self organizing map) learning on
som-dimensions: NO DOCSTRING
som-fitness: NO DOCSTRING
som-initialize-linear: NO DOCSTRING
som-neighborhoods: NO DOCSTRING
som-update-cells: NO DOCSTRING
som-update-weights: NO DOCSTRING
generate-population-query: Wrap the ClojureQL statement in a population query.
insert-dataset: NO DOCSTRING
read-dataframe: Read a single frame from the database table
read-dataset: Lazily read a dataset for the given ClojureQL query.
window: NO DOCSTRING
save-svg: Save a chart object as an SVG document.
all-fn-list: NO DOCSTRING
chain-list: NO DOCSTRING
chainable?: NO DOCSTRING
conv-qtnt: NO DOCSTRING
deriv: Macro for symbolic differentiation. with 2 args, takes 1st degree deriv.
deriv*: main sub-function for differentiation. with 2 args, takes 1st degree deriv.
deriv-fn: Examples:
deriv-fn*: Examples:
difference?: NO DOCSTRING
expnt: NO DOCSTRING
expnt?: NO DOCSTRING
fn-list: NO DOCSTRING
make-expnt: NO DOCSTRING
make-prod: NO DOCSTRING
make-sum: assemble a sum expression properly
product?: NO DOCSTRING
quotient?: NO DOCSTRING
reduce-expr: NO DOCSTRING
same-var?: on one arg, checks if variable, ie a symbol not in our list, on two,
sum?: NO DOCSTRING
tree-subst: Examples:
$$: This is the equivalent of :: in xts. That is, it slices out the timeseries between ind-1 and ind-2. These are any values that can be coerced into clj-time values.
JodaCoercible: NO DOCSTRING
aligned?: Is the :index column identical for all zs.
coredata: Return the :rows of a dataset, with :index dissoc'd.
in?: true if seq contains elm.
index-only?: Returns true if a map contains only an index
lag: Return the timeseries lagged by n units or 1 if not specified. No time calculations
nil-row: Returns a map with the same keys as x, but with nils for
order: Order a zoo so that the :index is increasing in time.
roll-apply: A generic function for applying a function to rolling window of a collection.
roll-max: Returns the rolling max of the previous n elements.
roll-mean: Returns the unweighted mean of the previous n data points.
roll-median: Returns the rolling median of the previous n elements.
roll-min: Returns the rolling min of the previous n elements.
row-order: Orders a seq of rows by index
to-date-time: NO DOCSTRING
within-zoo?: Is t between the first and last indices.
zoo: Return the given dataset as a zoo value which is simply a dataset
zoo-apply: Behave as for roll-apply but accept a zoo and a single column upon which to roll-apply f. Returns a zoo of the same length as input zoo with pre-pended nils
zoo-row-map: Accept a number of aligned zoo object and pass them row-wise into f, return a zoo. f must accept and return maps. The :index column is stripped out before f is applied, and then replaced afterwards with the :index of the first.
zoo-row-map-: Accept a number of aligned zoo object and pass them row-wise into f, return a seq of maps of the output of the output. f must accept and return maps. The :index column is stripped out before f is applied, and then replaced afterwards.
zoo-row-map-occupied: zoo-row-map- and remove the empties. This returns a seq of maps
zoo-simplify: Returns a vector if 1 row, else identity