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The [https://github.com/gnu-octave/statistics/ statistics package] is part of the [https://gnu-octave.github.io/packages/ Octave Packages]. Since version [https://github.com/gnu-octave/statistics/releases/tag/release-1.5.0 1.5.0], the statistics package requires Octave version 6.1 or higher. From Octave v7.2 or later, you can install the latest statistics package (currently 1.5.3) with the following command:
The [https://github.com/gnu-octave/statistics/ statistics package] is part of the [https://gnu-octave.github.io/packages/ Octave Packages].
 
<code>pkg install -forge statistics</code>
 
The following sections provide an overview of the functions available in the statistics package sorted alphabetically and arranged in groups similarly to the package's INDEX file. the '''TODO''' subsections are only informative of the current development plans for the forthcoming releases and they are not intended for reporting bugs, missing features or incompatibilities. Please report these in the [https://github.com/gnu-octave/statistics statistics repository] at GitHub.
 
== Clustering ==
 
=== Available functions ===
 
The following table lists the available functions for clustering data.
 
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/cluster.html cluster]
| Define clusters from an agglomerative hierarchical cluster tree.
|-
| [https://gnu-octave.github.io/statistics/clusterdata.html clusterdata]
| Wrapper function for 'linkage' and 'cluster'.
|-
| [https://gnu-octave.github.io/statistics/cmdscale.html cmdscale]
| Classical multidimensional scaling of a matrix.
|-
| [https://gnu-octave.github.io/statistics/confusionmat.html confusionmat]
| Compute a confusion matrix for classification problems.
|-
| [https://gnu-octave.github.io/statistics/ConfusionMatrixChart.html ConfusionMatrixChart]
| Compute a ConfusionMatrixChart class object.
|-
| [https://gnu-octave.github.io/statistics/cophenet.html cophenet]
| Compute the cophenetic correlation coefficient.
|-
| [https://gnu-octave.github.io/statistics/evalclusters.html evalclusters]
| Create a clustering evaluation object to find the optimal number of clusters.
|-
| [https://gnu-octave.github.io/statistics/inconsistent.html inconsistent]
| Compute the inconsistency coefficient for each link of a hierarchical cluster tree.
|-
| [https://gnu-octave.github.io/statistics/kmeans.html kmeans]
| Perform a K-means clustering of an NxD matrix.
|-
| [https://gnu-octave.github.io/statistics/linkage.html linkage]
| Produce a hierarchical clustering dendrogram.
|-
| [https://gnu-octave.github.io/statistics/mhsample.html mahal]
| Mahalanobis' D-square distance.
|-
| [https://gnu-octave.github.io/statistics/mhsample.html mhsample]
| Draws NSAMPLES samples from a target stationary distribution PDF using Metropolis-Hastings algorithm.
|-
| [https://gnu-octave.github.io/statistics/optimalleaforder.html optimalleaforder]
| Compute the optimal leaf ordering of a hierarchical binary cluster tree.
|-
| [https://gnu-octave.github.io/statistics/pdist.html pdist]
| Return the distance between any two rows in X.
|-
| [https://gnu-octave.github.io/statistics/pdist2.html pdist2]
| Compute pairwise distance between two sets of vectors.
|-
| [https://gnu-octave.github.io/statistics/procrustes.html procrustes]
| Procrustes Analysis.
|-
| [https://gnu-octave.github.io/statistics/slicesample.html slicesample]
| Draws NSAMPLES samples from a target stationary distribution PDF using slice sampling of Radford M. Neal.
|-
| [https://gnu-octave.github.io/statistics/squareform.html squareform]
| Interchange between distance matrix and distance vector formats.
|}
 
=== TODO list ===
 
Missing functions:
 
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* <code>procrustes</code>
</div>
 
== Data Manipulation ==
 
=== Available functions ===
 
The following table lists the available functions for data manipulation.
 
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/combnk.html combnk]
| Return all combinations of K elements in DATA.
|-
| [https://gnu-octave.github.io/statistics/crosstab.html crosstab]
| Create a cross-tabulation (contingency table) T from data vectors.
|-
| [https://gnu-octave.github.io/statistics/datasample.html datasample]
| Randomly sample data.
|-
| [https://gnu-octave.github.io/statistics/fillmissing.html fillmissing]
| Replace missing entries of array A either with values in v or as determined by other specified methods.
|-
| [https://gnu-octave.github.io/statistics/grp2idx.html grp2idx]
| Get index for group variables.
|-
| [https://gnu-octave.github.io/statistics/ismissing.html ismissing]
| Find missing data in a numeric or string array.
|-
| [https://gnu-octave.github.io/statistics/normalise_distribution.html normalise_distribution]
|  Transform a set of data so as to be N(0,1) distributed according to an idea by van Albada and Robinson.
|-
| [https://gnu-octave.github.io/statistics/rmmissing.html rmmissing]
| Remove missing or incomplete data from an array.
|-
| [https://gnu-octave.github.io/statistics/standardizeMissing.html standardizeMissing]
| Replace data values specified by indicator in A by the standard ’missing’ data value for that data type.
|-
| [https://gnu-octave.github.io/statistics/tabulate.html tabulate]
| Compute a frequency table.
|}
 
== Descriptive Statistics ==
 
=== Available functions ===
 
The following table lists the available functions for descriptive statistics.
 
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/cl_multinom.html cl_multinom]
| Confidence level of multinomial portions.
|-
| [https://gnu-octave.github.io/statistics/geomean.html geomean]
| Compute the geometric mean.
|-
| [https://gnu-octave.github.io/statistics/grpstats.html grpstats]
| Compute summary statistics by group. Fully MATLAB compatible.
|-
| [https://gnu-octave.github.io/statistics/harmmean.html harmmean]
| Compute the harmonic mean.
|-
| [https://gnu-octave.github.io/statistics/jackknife.html jackknife]
| Compute jackknife estimates of a parameter taking one or more given samples as parameters.
|-
| [https://gnu-octave.github.io/statistics/mean.html mean]
| Compute the mean. Fully MATLAB compatible.
|-
| [https://gnu-octave.github.io/statistics/median.html median]
| Compute the median. Fully MATLAB compatible.
|-
| [https://gnu-octave.github.io/statistics/nanmax.html nanmax]
| Find the maximal element while ignoring NaN values.
|-
| [https://gnu-octave.github.io/statistics/nanmin.html nanmin]
| Find the minimal element while ignoring NaN values.
|-
| [https://gnu-octave.github.io/statistics/nansum.html nansum]
| Compute the sum while ignoring NaN values.
|-
| [https://gnu-octave.github.io/statistics/std.html std]
| Compute the standard deviation. Fully MATLAB compatible.
|-
| [https://gnu-octave.github.io/statistics/trimmean.html trimmean]
| Compute the trimmed mean.
|-
| [https://gnu-octave.github.io/statistics/var.html std]
| Compute the variance. Fully MATLAB compatible.
|}
 
=== In external packages ===
 
<code>bootci</code>, <code>bootstrp</code> are implemented in the [https://gnu-octave.github.io/packages/statistics-resampling statistics-resampling] package.
 
=== Shadowing Octave core functions ===
 
The following functions will shadow the respective core functions until Octave 9.
 
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* <code>mean</code>
* <code>median</code>
* <code>std</code>
* <code>var</code>
</div>
 
=== TODO list ===
 
Update <code>trimmean</code> function to be fully MATLAB compatible.
 
Re-introduce the <code>nan*</code> functions implemented in C++ with the <code>"all"</code> and <code>"vecdim"</code> options.
 
Re-implement the following functions from core Octave, as shadowing functions with updated functionality regarding the <code>"all"</code>, <code>"omitnan"</code>, and <code>"vecdim"</code> options, with the intend to be included in Octave 9.
 
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* <code>cov</code>
* <code>mad</code>
* <code>meansq</code>
* <code>mode</code>
* <code>moment</code>
</div>


== Distributions ==
== Distributions ==
=== Available functions ===


The following table lists the '''cdf''', '''icdf''', '''pdf''', and '''random''' functions available in the statistics package. Since version [https://github.com/gnu-octave/statistics/releases/tag/release-1.5.3 1.5.3], all CDFs support the "upper" option for evaluating the complement of the respective CDF.
The following table lists the '''cdf''', '''icdf''', '''pdf''', and '''random''' functions available in the statistics package. Since version [https://github.com/gnu-octave/statistics/releases/tag/release-1.5.3 1.5.3], all CDFs support the "upper" option for evaluating the complement of the respective CDF.
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! Random Generator
! Random Generator
|-
|-
| [https://en.wikipedia.org/wiki/Birnbaum%E2%80%93Saunders_distribution Birnbaum–Saunders]
| https://en.wikipedia.org/wiki/Birnbaum%E2%80%93Saunders_distribution | Birnbaum–Saunders
| bbscdf
| bbscdf
| bbsinv
| bbsinv
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| bbsrnd
| bbsrnd
|-
|-
| [https://en.wikipedia.org/wiki/Beta_distribution Beta]
| https://en.wikipedia.org/wiki/Beta_distribution | Beta
| betacdf
| betacdf
| betainv
| betainv
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| betarndbivariate  
| betarndbivariate  
|-
|-
| [[https://en.wikipedia.org/wiki/Binomial_distribution Binomial]
| https://en.wikipedia.org/wiki/Binomial_distribution | Binomial
| binocdf
| binocdf
| binoinv
| binoinv
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| binornd  
| binornd  
|-
|-
| [https://en.wikipedia.org/wiki/Joint_probability_distribution Bivariate Normal]
| Bivariate
| bvncdf
| bvncdf
|
|
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|
|
|-
|-
| [https://en.wikipedia.org/wiki/Joint_probability_distribution Bivariate Student's <i>t</i>]
| https://www.mathworks.com/help/stats/burr-type-xii-distribution.html | Burr Type XII
| bvtcdf
|
|
|
|-
| [https://www.mathworks.com/help/stats/burr-type-xii-distribution.html Burr Type XII]
| burrcdf
| burrcdf
| burrinv
| burrinv
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| burrrnd
| burrrnd
|-
|-
| [https://en.wikipedia.org/wiki/Cauchy_distribution Cauchy]
| https://en.wikipedia.org/wiki/Cauchy_distribution | Cauchy
| cauchy_cdf
| cauchy_cdf
| cauchy_inv
| cauchy_inv
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| cauchy_rnd
| cauchy_rnd
|-
|-
| [https://en.wikipedia.org/wiki/Chi-squared_distribution Chi-squared]
| https://en.wikipedia.org/wiki/Chi-squared_distribution | Chi-squared
| chi2cdf
| chi2cdf
| chi2inv
| chi2inv
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| chi2rnd
| chi2rnd
|-
|-
| [https://en.wikipedia.org/wiki/Copula_(probability_theory) Copula Family]
| Copula Family
| copulacdf
| copulacdf
| copulainv
| copulainv
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| copularnd
| copularnd
|-
|-
| [https://en.wikipedia.org/wiki/Gumbel_distribution Extreme Value]
| Extreme Value
| evcdf
| evcdf
| evinv
| evinv
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| evrnd
| evrnd
|-
|-
| [https://en.wikipedia.org/wiki/Exponential_distribution Exponential]
| https://en.wikipedia.org/wiki/Exponential_distribution | Exponential
| expcdf
| expcdf
| expinv
| expinv
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| exprnd
| exprnd
|-
|-
| [https://en.wikipedia.org/wiki/F-distribution F]
| https://en.wikipedia.org/wiki/F-distribution | F
| fcdf
| fcdf
| finv
| finv
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| frnd
| frnd
|-
|-
| [https://en.wikipedia.org/wiki/Gamma_distribution Gamma]
| https://en.wikipedia.org/wiki/Gamma_distribution | Gamma
| gamcdf
| gamcdf
| gaminv
| gaminv
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| gamrnd
| gamrnd
|-
|-
| [https://en.wikipedia.org/wiki/Geometric_distribution Geometric]
| https://en.wikipedia.org/wiki/Geometric_distribution | Geometric
| geocdf
| geocdf
| geoinv
| geoinv
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| geornd
| geornd
|-
|-
| [https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution Generalized Extreme Value]
| https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution | Generalized Extreme Value
| gevcdf
| gevcdf
| gevinv
| gevinv
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| gevrnd
| gevrnd
|-
|-
| [https://en.wikipedia.org/wiki/Generalized_Pareto_distribution Generalized Pareto]
| https://en.wikipedia.org/wiki/Generalized_Pareto_distribution | Generalized Pareto
| gpcdf
| gpcdf
| gpinv
| gpinv
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| gprnd
| gprnd
|-
|-
| [https://en.wikipedia.org/wiki/Hypergeometric_distribution Hypergeometric]
| https://en.wikipedia.org/wiki/Hypergeometric_distribution | Hypergeometric
| hygecdf
| hygecdf
| hygeinv
| hygeinv
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| hygernd
| hygernd
|-
|-
| [https://en.wikipedia.org/wiki/Inverse-Wishart_distribution Inverse-Wishart]
| https://en.wikipedia.org/wiki/Inverse-Wishart_distribution | Inverse-Wishart
|
|
|
|
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| iwishrnd
| iwishrnd
|-
|-
| [https://en.wikipedia.org/wiki/Johnson%27s_SU-distribution Johnson's SU]
| https://en.wikipedia.org/wiki/Johnson%27s_SU-distribution | Johnson's SU
| jsucdf
| jsucdf
|
|
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|
|
|-
|-
| [https://en.wikipedia.org/wiki/Laplace_distribution Laplace]
| https://en.wikipedia.org/wiki/Laplace_distribution | Laplace
| laplace_cdf
| laplace_cdf
| laplace_inv
| laplace_inv
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| laplace_rnd
| laplace_rnd
|-
|-
| [https://en.wikipedia.org/wiki/Logistic_distribution Logistic]
| https://en.wikipedia.org/wiki/Logistic_distribution | Logistic
| logistic_cdf
| logistic_cdf
| logistic_inv
| logistic_inv
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| logistic_rnd
| logistic_rnd
|-
|-
| [https://en.wikipedia.org/wiki/Log-normal_distribution Log-normal]
| https://en.wikipedia.org/wiki/Log-normal_distribution | Log-normal
| logncdf
| logncdf
| logninv
| logninv
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| lognrnd
| lognrnd
|-
|-
| [https://en.wikipedia.org/wiki/Multinomial_distribution Multinomial]
| https://en.wikipedia.org/wiki/Multinomial_distribution | Multinomial
|
|
|
|
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| mnrnd
| mnrnd
|-
|-
| [https://en.wikipedia.org/wiki/Multivariate_normal_distribution Multivariate Normal]
| https://en.wikipedia.org/wiki/Multivariate_normal_distribution | Multivariate Normal
| mvncdf
| mvncdf
| mvninv
| mvninv
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| mvnrnd
| mvnrnd
|-
|-
| [https://en.wikipedia.org/wiki/Multivariate_t-distribution Multivariate Student's <i>t</i>]
| https://en.wikipedia.org/wiki/Multivariate_t-distribution | Multivariate Student's T
| mvtcdf mvtcdfqmc
| mvtcdf mvtcdfqmc
| mvtinv
| mvtinv
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| mvtrnd
| mvtrnd
|-
|-
| [https://en.wikipedia.org/wiki/Nakagami_distribution Nakagami]
| https://en.wikipedia.org/wiki/Nakagami_distribution | Nakagami
| nakacdf
| nakacdf
| nakainv
| nakainv
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| nakarnd
| nakarnd
|-
|-
| [https://en.wikipedia.org/wiki/Negative_binomial_distribution Negative Binomial]
| https://en.wikipedia.org/wiki/Negative_binomial_distribution | Negative Binomial
| nbincdf
| nbincdf
| nbininv
| nbininv
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| nbinrnd
| nbinrnd
|-
|-
| [https://en.wikipedia.org/wiki/Noncentral_F-distribution Noncentral F]
| https://en.wikipedia.org/wiki/Noncentral_F-distribution | Noncentral F
| ncfcdf
| ncfcdf
| ncfinv
| ncfinv
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| ncfrnd
| ncfrnd
|-
|-
| [https://en.wikipedia.org/wiki/Noncentral_t-distribution Noncentral Student's <i>t</i>]
| https://en.wikipedia.org/wiki/Noncentral_t-distribution | Noncentral Student's T
| nctcdf
| nctcdf
| nctinv
| nctinv
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| nctrnd
| nctrnd
|-
|-
| [https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution Noncentral Chi-squared]
| https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution | Noncentral Chi-squared
| ncx2cdf
| ncx2cdf
| ncx2inv
| ncx2inv
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| ncx2rnd
| ncx2rnd
|-
|-
| [https://en.wikipedia.org/wiki/Normal_distribution Normal]
| https://en.wikipedia.org/wiki/Normal_distribution | Normal
| normcdf
| normcdf
| norminv
| norminv
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| normrnd
| normrnd
|-
|-
| [https://en.wikipedia.org/wiki/Poisson_distribution Poisson]
| https://en.wikipedia.org/wiki/Poisson_distribution | Poisson
| poisscdf
| poisscdf
| poissinv
| poissinv
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| poissrnd
| poissrnd
|-
|-
| [https://en.wikipedia.org/wiki/Rayleigh_distribution Rayleigh]
| https://en.wikipedia.org/wiki/Rayleigh_distribution | Rayleigh
| raylcdf
| raylcdf
| raylinv
| raylinv
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| raylrnd
| raylrnd
|-
|-
| [https://en.wikipedia.org/wiki/Normal_distribution#Standard_normal_distribution Standard Normal]
| https://en.wikipedia.org/wiki/Normal_distribution#Standard_normal_distribution | Standard Normal
| stdnormal_cdf
| stdnormal_cdf
| stdnormal_inv
| stdnormal_inv
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| stdnormal_rnd
| stdnormal_rnd
|-
|-
| [https://en.wikipedia.org/wiki/Student%27s_t-distribution Student's <i>t</i>]
| https://en.wikipedia.org/wiki/Student%27s_t-distribution | Student's T
| tcdf
| tcdf
| tinv
| tinv
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| trnd
| trnd
|-
|-
| [https://en.wikipedia.org/wiki/Triangular_distribution Triangular]
| https://en.wikipedia.org/wiki/Triangular_distribution | Triangular
| tricdf
| tricdf
| triinv
| triinv
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| trirnd
| trirnd
|-
|-
| [https://en.wikipedia.org/wiki/Discrete_uniform_distribution Discrete Uniform]
| https://en.wikipedia.org/wiki/Discrete_uniform_distribution | Discrete Uniform
| unidcdf
| unidcdf
| unidinv
| unidinv
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| unidrnd
| unidrnd
|-
|-
| [https://en.wikipedia.org/wiki/Continuous_uniform_distribution Continuous Uniform]
| https://en.wikipedia.org/wiki/Continuous_uniform_distribution | Continuous Uniform
| unifcdf
| unifcdf
| unifinv
| unifinv
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| unifrnd
| unifrnd
|-
|-
| [https://en.wikipedia.org/wiki/Von_Mises_distribution von Mises]
| https://en.wikipedia.org/wiki/Von_Mises_distribution | von Mises
| vmcdf
| vmcdf
|
|
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| vmrnd
| vmrnd
|-
|-
| [https://en.wikipedia.org/wiki/Weibull_distribution Weibull]
| https://en.wikipedia.org/wiki/Weibull_distribution | Weibull
| wblcdf
| wblcdf
| wblinv
| wblinv
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| wblrnd
| wblrnd
|-
|-
| [https://en.wikipedia.org/wiki/Wiener_process Wiener process]
| https://en.wikipedia.org/wiki/Wiener_process | Wiener process
|
|
|
|
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| wienrnd
| wienrnd
|-
|-
| [https://en.wikipedia.org/wiki/Wishart_distribution Wishart]
| https://en.wikipedia.org/wiki/Wishart_distribution | Wishart
|
|
|
|
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|}
|}


=== Distribution Fitting ===
Functions available for estimating parameters and the negative log-likelihood for certain distributions.
{| class="wikitable"
! Distribution Name
! Parameter Estimation
! Negativel Log-likelihood
|-
| Extreme Value
| evfit
| evlike
|-
| Exponential
| expfit
| explike
|-
| Gamma
| gamfit
| gamlike
|-
| Generalized Extreme Value
| gevfit_lmom gevfit
| gevlike
|-
| Generalized Pareto
| gpfit
| gplike
|-
| Normal
|
| normlike
|}
=== Distribution Statistics ===
Functions available for computing ''mean'' and ''variance'' from distribution parameters.
<div style="column-count:4;-moz-column-count:4;-webkit-column-count:4">
* <code>betastat</code>
* <code>binostat</code>
* <code>chi2stat</code>
* <code>evstat</code>
* <code>expstat</code>
* <code>fstat</code>
* <code>gamstat</code>
* <code>geostat</code>
* <code>gevstat</code>
* <code>gpstat</code>
* <code>hygestat</code>
* <code>lognstat</code>
* <code>nbinstat</code>
* <code>ncfstat</code>
* <code>nctstat</code>
* <code>ncx2stat</code>
* <code>normstat</code>
* <code>poisstat</code>
* <code>raylstat</code>
* <code>fitgmdist</code>
* <code>tstat</code>
* <code>unidstat</code>
* <code>unifstat</code>
* <code>wblstat</code>
</div>
== Experimental Design ==
=== Available functions ===
Functions available for computing design matrices.
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/fullfact.html fullfact]
| Full factorial design.
|-
| [https://gnu-octave.github.io/statistics/ff2n.html ff2n]
| Two-level full factorial design.
|-
| [https://gnu-octave.github.io/statistics/sigma_pts.html sigma_pts]
| Calculates 2*N+1 sigma points in N dimensions.
|-
| [https://gnu-octave.github.io/statistics/x2fx.html x2fx]
| Convert predictors to design matrix.
|}
== Machine Learning ==
=== Available functions ===
The following table lists the available functions.
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/hmmestimate.html hmmestimate]
| Estimation of a hidden Markov model for a given sequence.
|-
| [https://gnu-octave.github.io/statistics/hmmgenerate.html hmmgenerate]
| Output sequence and hidden states of a hidden Markov model.
|-
| [https://gnu-octave.github.io/statistics/hmmviterbi.html hmmviterbi]
| Viterbi path of a hidden Markov model.
|-
| [https://gnu-octave.github.io/statistics/svmpredict.html svmpredict]
| Perform a K-means clustering of an NxD matrix.
|-
| [https://gnu-octave.github.io/statistics/svmtrain.html svmtrain]
| Produce a hierarchical clustering dendrogram.
|}
=== TODO list ===
Update <code>svmpredict</code> and <code>svmtrain</code> to libsvm 3.0.
Missing functions:
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* <code>hmmdecode</code>
* <code>hmmtrain</code>
</div>


== Model Fitting ==
=== Can be reused in other functions ===
 
=== Available functions ===
 
Functions available for fitting or evaluating statistical models.


{| class="wikitable"
{| class="wikitable"
! Function
!forge function
! Description
!matlab counterpart
|-
|-
| [https://gnu-octave.github.io/statistics/crossval.html crossval]
|anderson_darling_test
| Perform cross validation on given data.
|adtest
|-
|-
| [https://gnu-octave.github.io/statistics/fitgmdist.html fitgmdist]
|bbscdf
| Fit a Gaussian mixture model with K components to DATA.
|BirnbaumSaundersDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/fitlm.html fitlm]
|bbsinv
| Regress the continuous outcome (i.e.  dependent variable) Y on continuous or categorical predictors (i.e.  independent variables) X by minimizing the sum-of-squared residuals.
|BirnbaumSaundersDistribution class
|}
 
=== Cross Validation ===
 
Class of set partitions for cross-validation, used in crossval
 
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* @cvpartition/cvpartition
* @cvpartition/display
* @cvpartition/get
* @cvpartition/repartition
* @cvpartition/set
* @cvpartition/test
* @cvpartition/training
</div>
 
=== TODO list ===
 
Missing functions:
 
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* <code>anova</code>
* <code>manova</code>
</div>
 
== Hypothesis Testing ==
 
=== Available functions ===
 
Functions available for hypothesis testing
 
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/adtest.html adtest]
| Anderson-Darling goodness-of-fit hypothesis test.
|-
| [https://gnu-octave.github.io/statistics/anova1.html anova1]
| Perform a one-way analysis of variance (ANOVA)
|-
|-
| [https://gnu-octave.github.io/statistics/anova2.html anova2]
|bbspdf
| Performs two-way factorial (crossed) or a nested analysis of variance (ANOVA) for balanced designs.
|BirnbaumSaundersDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/anovan.html anovan]
|bbsrnd
| Perform a multi (N)-way analysis of (co)variance (ANOVA or ANCOVA) to evaluate the effect of one or more categorical or continuous predictors (i.e.  independent variables) on a continuous outcome (i.e.  dependent variable).
|BirnbaumSaundersDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/bartlett_test.html bartlett_test]
|binotest
| Perform a Bartlett test for the homogeneity of variances.
|binofit
|-
|-
| [https://gnu-octave.github.io/statistics/barttest.html barttest]
|burrcdf
| Bartlett's test of sphericity for correlation.
|BurrDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/binotest.html binotest]
|burrinv
| Test for probability P of a binomial sample
|BurrDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/chi2gof.html chi2gof]
|burrpdf
| Chi-square goodness-of-fit test.
|BurrDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/chi2test.html chi2test]
|burrrnd
| Perform a chi-squared test (for independence or homogeneity).
|BurrDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/correlation_test.html correlation_test]
|nakacdf
| Perform a correlation coefficient test whether two samples x and y come from uncorrelated populations.
|NakagamiDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/fishertest.html fishertest]
|nakainv
| Fisher’s exact test.
|NakagamiDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/friedman.html friedman]
|nakapdf
| Performs the nonparametric Friedman's test to compare column effects in a two-way layout.
|NakagamiDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/hotelling_t2test.html hotelling_t2test]
|nakarnd - should be used to implement the
| Compute Hotelling's T^2 ("T-squared") test for a single sample or two dependent samples (paired-samples).
|NakagamiDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/hotelling_t2test2.html hotelling_t2test2]
|regress_gp
| Compute Hotelling's T^2 ("T-squared") test for two independent samples.
|RegressionGP class
|-
|-
| [https://gnu-octave.github.io/statistics/kruskalwallis.html kruskalwallis]
|repanova
| Perform a Kruskal-Wallis test, the non-parametric alternative of a one-way analysis of variance (ANOVA).
|RepeatedMeasuresModel.ranova
|-
|-
| [https://gnu-octave.github.io/statistics/kstest.html kstest]
|tricdf
| Single sample Kolmogorov-Smirnov (K-S) goodness-of-fit hypothesis test.
|TriangularDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/kstest2.html kstest2]
|triinv
| Two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test.
|TriangularDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/levene_test.html levene_test]
|tripdf
| Perform a Levene's test for the homogeneity of variances.
|TriangularDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/manova1.html manova1]
|trirnd
| One-way multivariate analysis of variance (MANOVA).
|TriangularDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/multcompare.html multcompare]
|logistic_cdf
| Perform posthoc multiple comparison tests or p-value adjustments to control the family-wise error rate (FWER) or false discovery rate (FDR).
|LogisticDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/ranksum.html ranksum]
|logistic_inv
| Wilcoxon rank sum test for equal medians.  This test is equivalent to a Mann-Whitney U-test.
|LogisticDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/regression_ftest.html regression_ftest]
|logistic_pdf
| F-test for General Linear Regression Analysis
|LogisticDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/regression_ttest.html regression_ttest]
|logistic_rnd
| Perform a linear regression t-test.
|LogisticDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/runstest.html runstest]
|stdnormal_cdf
| Runs test for detecting serial correlation in the vector X.
|NormalDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/sampsizepwr.html sampsizepwr]
|stdnormal_inv
| Sample size and power calculation for hypothesis test.
|NormalDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/signtest.html signtest]
|stdnormal_pdf
| Test for median.
|NormalDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/ttest.html ttest]
|stdnormal_rnd
| Test for mean of a normal sample with unknown variance or a paired-sample t-test.
|NormalDistribution class
|-
|-
| [https://gnu-octave.github.io/statistics/ttest2.html ttest2]
|anova
| Perform a two independent samples t-test.
|anova method in different *Model classes
|-
|-
| [https://gnu-octave.github.io/statistics/vartest.html vartest]
|manova
| One-sample test of variance.
|manova methods in different *Model classes
|-
|-
| [https://gnu-octave.github.io/statistics/vartest2.html vartest2]
|bartlett_test
| Two-sample F test for equal variances.
|barttest
|-
|-
| [https://gnu-octave.github.io/statistics/vartestn.html vartestn]
|kolmogorov_smirnov_test
| Test for equal variances across multiple groups.
|ktest
|-
|-
| [https://gnu-octave.github.io/statistics/ztest.html ztest]
|kolmogorov_smirnov_test_2
| One-sample Z-test.
|ktest2
|-
|-
| [https://gnu-octave.github.io/statistics/ztest2.html ztest2]
|kruskal_wallis_test
| Two proportions Z-test.
|kruskalwallis
|}
|}


=== TODO list ===
=== Ready to go ===


Missing functions:
These functions seem to be Matlab compatible


<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
<div style="column-count:4;-moz-column-count:4;-webkit-column-count:4">
* <code>fishertest</code>
* anovan
* <code>meanEffectSize</code>
* betastat
* binostat
* binotest
* canoncorr
* caseread
* casewrite
* cdf
* chi2stat
* cmdscale
* combnk
* copulacdf
* copulapdf
* copularnd
* crossval
* @cvpartition
* dendrogram
* expstat
* ff2n
* fitgmdist
* fstat
* fullfact
* gamfit
* gamlike
* gamstat
* geomean
* geostat
* gevcdf
* gevfit
* gevinv
* gevlike
* gevpdf
* gevrnd
* gevstat
* gmdistribution
* grp2idx
* harmmean
* hist3
* histfit
* hmmestimate
* hmmgenerate
* hmmviterbi
* hygestat
* iwishrnd
* jackknife
* kmeans
* linkage
* lognstat
* mad
* mahal
* mnpdf
* mnrnd
* mvncdf
* mvnpdf
* mvnrnd
* mvtcdf
* mvtpdf
* mvtrnd
* nanmax
* nanmean
* nanmedian
* nanmin
* nanstd
* nansum
* nanvar
* nbinstat
* normplot
* normstat
* pcacov
* pcares
* pdf
* pdist2
* pdist
* plsregress
* poisstat
* random
* randsample
* raylcdf
* raylinv
* raylpdf
* raylrnd
* raylstat
* regress
* signtest
* squareform
* stepwisefit
* tabulate
* tblread
* tblwrite
* trimmean
* tstat
* ttest2
* ttest
* unidstat
* unifstat
* vartest2
* vartest
* wblstat
* wishrnd
* ztest
* prctile
* qqplot
* betacdf
* betainv
* betapdf
* betarnd
* binocdf
* binoinv
* binopdf
* binornd
* chi2cdf
* chi2inv
* chi2pdf
* chi2rnd
* expcdf
* expinv
* exppdf
* exprnd
* fcdf
* finv
* fpdf
* frnd
* gamcdf
* gaminv
* gampdf
* gamrnd
* geocdf
* geoinv
* geopdf
* geornd
* hygecdf
* hygeinv
* hygepdf
* hygernd
* logncdf
* logninv
* lognpdf
* lognrnd
* nbincdf
* nbininv
* nbinpdf
* nbinrnd
* normcdf
* norminv
* normpdf
* normrnd
* poisscdf
* poissinv
* poisspdf
* poissrnd
* tcdf
* tinv
* tpdf
* trnd
* unidcdf
* unidinv
* unidpdf
* unidrnd
* unifcdf
* unifinv
* unifpdf
* unifrnd
* wblcdf
* wblinv
* wblpdf
* wblrnd
</div>
</div>


== Plotting ==
=== In external packages ===


=== Available functions ===
bootci, bootstrp are implemented in the [https://gnu-octave.github.io/packages/statistics-bootstrap statistics-bootstrap] package


The following table lists the available functions for plotting data.
== Development ==


{| class="wikitable"
Follows an incomplete list of stuff missing in the statistics package to be matlab compatible. Bugs are not listed here, [https://savannah.gnu.org/bugs/?func=search&group=octave search] and [https://savannah.gnu.org/bugs/?func=additem&group=octave report] them on the bug tracker instead.
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/boxplot.html boxplot]
| Produce a box plot.
|-
| [https://gnu-octave.github.io/statistics/cdfplot.html cdfplot]
| Display an empirical cumulative distribution function.
|-
| [https://gnu-octave.github.io/statistics/confusionchart.html confusionchart]
| Display a chart of a confusion matrix.
|-
| [https://gnu-octave.github.io/statistics/dendrogram.html dendrogram]
| Plot a dendrogram of a hierarchical binary cluster tree.
|-
| [https://gnu-octave.github.io/statistics/ecdf.html ecdf]
| Empirical (Kaplan-Meier) cumulative distribution function.
|-
| [https://gnu-octave.github.io/statistics/gscatter.html gscatter]
| Draw a scatter plot with grouped data.
|-
| [https://gnu-octave.github.io/statistics/histfit.html histfit]
| Plot histogram with superimposed fitted normal density.
|-
| [https://gnu-octave.github.io/statistics/hist3.html hist3]
| Produce bivariate (2D) histogram counts or plots.
|-
| [https://gnu-octave.github.io/statistics/manovacluster.html manovacluster]
| Cluster group means using manova1 output.
|-
| [https://gnu-octave.github.io/statistics/normplot.html normplot]
| Produce normal probability plot of the data.
|-
| [https://gnu-octave.github.io/statistics/ppplot.html ppplot]
| Perform a PP-plot (probability plot).
|-
| [https://gnu-octave.github.io/statistics/qqplot.html qqplot]
| Perform a QQ-plot (quantile plot).
|-
| [https://gnu-octave.github.io/statistics/silhouette.html silhouette]
| Compute the silhouette values of clustered data and show them on a plot.
|-
| [https://gnu-octave.github.io/statistics/violin.html violin]
| Produce a Violin plot of the data.
|-
| [https://gnu-octave.github.io/statistics/wblplot.html wblplot]
| Plot a column vector DATA on a Weibull probability plot using rank regression.
|}


=== TODO list ===
{{Note|this entire section is about the current development version. If a Matlab function is missing from the list and does not appear on the current release of the package, confirm that is also missing in the [https://sourceforge.net/p/octave/statistics/ development sources] before adding it.}}


Missing functions:
=== Missing functions ===
 
<div style="column-count:4;-moz-column-count:4;-webkit-column-count:4">
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* ClassificationBaggedEnsemble
* <code>andrewsplot</code>
* ClassificationDiscriminant
* <code>bar3</code>
* ClassificationDiscriminant.fit
* <code>bar3h</code>
* ClassificationEnsemble
* <code>glyphplot</code>
* ClassificationKNN
* <code>gplotmatrix</code>
* ClassificationKNN.fit
* <code>parallelcoords</code>
* ClassificationPartitionedEnsemble
* ClassificationPartitionedModel
* ClassificationTree
* ClassificationTree.fit
* CompactClassificationDiscriminant
* CompactClassificationEnsemble
* CompactClassificationTree
* CompactRegressionEnsemble
* CompactRegressionTree
* CompactTreeBagger
* ExhaustiveSearcher
* GeneralizedLinearModel
* GeneralizedLinearModel.fit
* GeneralizedLinearModel.stepwise
* KDTreeSearcher
* LinearMixedModel
* LinearMixedModel.fit
* LinearMixedModel.fitmatrix
* LinearModel
* LinearModel.fit
* LinearModel.stepwise
* NaiveBayes
* NaiveBayes.fit
* NonLinearModel
* NonLinearModel.fit
* ProbDistUnivKernel
* ProbDistUnivParam
* RegressionBaggedEnsemble
* RegressionEnsemble
* RegressionPartitionedEnsemble
* RegressionPartitionedModel
* RegressionTree
* RegressionTree.fit
* TreeBagger
* addTerms
* addedvarplot
* addlevels
* adtest
* andrewsplot
* anova2
* ansaribradley
* aoctool
* barttest
* bbdesign
* betafit
* betalike
* binofit
* biplot
* candexch
* candgen
* capability
* capaplot
* ccdesign
* cdfplot
* cell2dataset
* chi2gof
* cholcov
* classify
* classregtree
* clustering.evaluation.CalinskiHarabaszEvaluation
* clustering.evaluation.DaviesBouldinEvaluation
* clustering.evaluation.GapEvaluation
* clustering.evaluation.SilhouetteEvaluation
* coefCI
* coefTest
* compact
* compare
* controlrules
* copulafit
* copulaparam
* copulastat
* cordexch
* corrcov
* covarianceParameters
* coxphfit
* createns
* crosstab
* dataset
* dataset2cell
* dataset2struct
* dataset2table
* datasetfun
* daugment
* dcovary
* designMatrix
* devianceTest
* dfittool
* disttool
* droplevels
* dummyvar
* dwtest
* ecdf
* ecdfhist
* evcdf
* evfit
* evinv
* evlike
* evpdf
* evrnd
* evstat
* export
* factoran
* fitdist
* fitensemble
* fitglm
* fitlm
* fitlme
* fitlmematrix
* fitnlm
* fitted
* fixedEffects
* fracfact
* fracfactgen
* friedman
* fsurfht
* gagerr
* getlabels
* getlevels
* gline
* glmfit
* glmval
* glyphplot
* gname
* gpcdf
* gpfit
* gpinv
* gplike
* gplotmatrix
* gppdf
* gprnd
* gpstat
* grpstats
* haltonset
* hmmdecode
* hmmtrain
* hougen
* icdf
* interactionplot
* invpred
* islevel
* isundefined
* jbtest
* johnsrnd
* join
* knnsearch
* ksdensity
* kstest
* kstest2
* labels
* lasso
* lassoPlot
* lassoglm
* levelcounts
* leverage
* lhsdesign
* lhsnorm
* lillietest
* linhyptest
* lognfit
* lognlike
* lsline
* mahal
* maineffectsplot
* makedist
* manova1
* manovacluster
* mat2dataset
* mdscale
* mergelevels
* mle
* mlecov
* mnrfit
* mnrval
* multcompare
* multivarichart
* mvregress
* mvregresslike
* nancov
* nbinfit
* ncfcdf
* ncfinv
* ncfpdf
* ncfrnd
* ncfstat
* nctcdf
* nctinv
* nctpdf
* nctrnd
* nctstat
* ncx2cdf
* ncx2inv
* ncx2rnd
* ncx2stat
* negloglik
* nlinfit
* nlintool
* nlmefit
* nlmefitsa
* nlparci
* nlpredci
* nnmf
* nominal
* normfit
* normlike
* normspec
* ordinal
* parallelcoords
* paramci
* paretotails
* partialcorr
* partialcorri
* pdf
* pearsrnd
* perfcurve
* plotAdded
* plotAdjustedResponse
* plotDiagnostics
* plotEffects
* plotInteraction
* plotResiduals
* plotSlice
* poissfit
* polytool
* ppca
* predict
* prob.BetaDistribution
* prob.BinomialDistribution
* prob.BirnbaumSaundersDistribution
* prob.BurrDistribution
* prob.ExponentialDistribution
* prob.ExtremeValueDistribution
* prob.GammaDistribution
* prob.GeneralizedExtremeValueDistribution
* prob.GeneralizedParetoDistribution
* prob.InverseGaussianDistribution
* prob.KernelDistribution
* prob.LogisticDistribution
* prob.LoglogisticDistribution
* prob.LognormalDistribution
* prob.MultinomialDistribution
* prob.NakagamiDistribution
* prob.NegativeBinomialDistribution
* prob.NormalDistribution
* prob.PiecewiseLinearDistribution
* prob.PoissonDistribution
* prob.RayleighDistribution
* prob.RicianDistribution
* prob.TriangularDistribution
* prob.UniformDistribution
* prob.WeibullDistribution
* prob.tLocationScaleDistribution
* probplot
* procrustes
* proflik
* qrandset
* qrandstream
* randomEffects
* randtool
* rangesearch
* ranksum
* raylfit
* rcoplot
* refcurve
* refline
* regstats
* relieff
* removeTerms
* residuals
* response
* ridge
* robustdemo
* robustfit
* rotatefactors
* rowexch
* rsmdemo
* rstool
* sampsizepwr
* scatterhist
* sequentialfs
* setlabels
* signrank
* sobolset
* statget
* statset
* step
* stepwise
* stepwiseglm
* stepwiselm
* struct2dataset
* surfht
* svmclassify
* svmtrain
* table2dataset
* tabulate
* tdfread
* tiedrank
* truncate
* unifit
* vartestn
* wblfit
* wbllike
* x2fx
* xptread
</div>
</div>


== Regression ==
=== Missing options ===
 
=== Available functions ===
 
The following table lists the available functions for regression analysis.


{| class="wikitable"
*explike: censoring and frequency aren't yet implemented
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/canoncorr.html canoncorr]
| Canonical correlation analysis.
|-
| [https://gnu-octave.github.io/statistics/cholcov.html cholcov]
| Cholesky-like decomposition for covariance matrix.
|-
| [https://gnu-octave.github.io/statistics/dcov.html dcov]
| Distance correlation, covariance and correlation statistics.
|-
| [https://gnu-octave.github.io/statistics/logistic_regression.html logistic_regression]
| Perform ordinal logistic regression.
|-
| [https://gnu-octave.github.io/statistics/monotone_smooth.html monotone_smooth]
| Produce a smooth monotone increasing approximation to a sampled functional dependence.
|-
| [https://gnu-octave.github.io/statistics/pca.html pca]
| Performs a principal component analysis on a data matrix.
|-
| [https://gnu-octave.github.io/statistics/pcacov.html pcacov]
| Perform principal component analysis on the NxN covariance matrix X
|-
| [https://gnu-octave.github.io/statistics/pcares.html pcares]
| Calculate residuals from principal component analysis.
|-
| [https://gnu-octave.github.io/statistics/plsregress.html plsregress]
| Calculate partial least squares regression using SIMPLS algorithm.
|-
| [https://gnu-octave.github.io/statistics/princomp.html princomp]
| Performs a principal component analysis on a NxP data matrix.
|-
| [https://gnu-octave.github.io/statistics/regress.html regress]
| Multiple Linear Regression using Least Squares Fit.
|-
| [https://gnu-octave.github.io/statistics/regress_gp.html regress_gp]
| Linear scalar regression using gaussian processes.
|-
| [https://gnu-octave.github.io/statistics/stepwisefit.html stepwisefit]
| Linear regression with stepwise variable selection.
|}
 
=== TODO list ===
 
Missing functions:
 
<div style="column-count:1;-moz-column-count:1;-webkit-column-count:1">
* <code>glmfit</code>
* <code>glmval</code>
* <code>mnrfit</code>
* <code>mnrval</code>
</div>
 
== Wrappers ==
 
=== Available functions ===
 
Functions available for wrapping other functions or group of functions.
 
{| class="wikitable"
! Function
! Description
|-
| [https://gnu-octave.github.io/statistics/cdf.html cdf]
| This is a wrapper for the NAMEcdf and NAME_cdf functions available in the statistics package.
|-
| [https://gnu-octave.github.io/statistics/icdf.html icdf]
| This is a wrapper for the NAMEinv and NAME_inv functions available in the statistics package.
|-
| [https://gnu-octave.github.io/statistics/pdf.html pdf]
| This is a wrapper for the NAMEpdf and NAME_pdf functions available in the statistics package.
|-
| [https://gnu-octave.github.io/statistics/random.html random]
| Generates pseudo-random numbers from a given one-, two-, or three-parameter distribution.
|}


[[Category:Packages]]
[[Category:Octave Forge]]
[[Category:Missing functions]]
[[Category:Missing functions]]
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