### statistics

1.6.3 2024-2-9

The Statistics package for GNU Octave.

Select Category:

### Clustering

`cluster` | Define clusters from an agglomerative hierarchical cluster tree.
`clusterdata` | Wrapper function for 'linkage' and 'cluster'.
`cmdscale` | Classical multidimensional scaling of a matrix.
`confusionmat` | Compute a confusion matrix for classification problems
`cophenet` | Compute the cophenetic correlation coefficient.
`crossval` | Perform cross validation on given data.
`evalclusters` | Create a clustering evaluation object to find the optimal number of clusters.
`inconsistent` | Compute the inconsistency coefficient for each link of a hierarchical cluster tree.
`kmeans` | Perform a K-means clustering of the NxD matrix DATA.
`knnsearch` | Find k-nearest neighbors from input data.
`linkage` | Produce a hierarchical clustering dendrogram.
`mahal` | Mahalanobis' D-square distance.
`mhsample` | Draws NSAMPLES samples from a target stationary distribution PDF using Metropolis-Hastings algorithm.
`optimalleaforder` | Compute the optimal leaf ordering of a hierarchical binary cluster tree.
`pdist` | Return the distance between any two rows in X.
`pdist2` | Compute pairwise distance between two sets of vectors.
`procrustes` | Procrustes Analysis.
`rangesearch` | Find all neighbors within specified distance from input data.
`slicesample` | Draws NSAMPLES samples from a target stationary distribution PDF using slice sampling of Radford M.
`squareform` | Interchange between distance matrix and distance vector formats.

### Classification Classes

`ClassificationKNN` | Create a ClassificationKNN class object containing a k-Nearest Neighbor classification model.
`ConfusionMatrixChart` | Create object CMC, a Confusion Matrix Chart object.

### Clustering Classes

`CalinskiHarabaszEvaluation` | A Calinski-Harabasz object to evaluate clustering solutions.
`ClusterCriterion` | A clustering evaluation object as created by 'evalclusters'.
`DaviesBouldinEvaluation` | A Davies-Bouldin object to evaluate clustering solutions.
`GapEvaluation` | A gap object to evaluate clustering solutions.
`SilhouetteEvaluation` | A silhouette object to evaluate clustering solutions.

### CVpartition (legacy class of set partitions for cross-validation, used in crossval)

`@cvpartition/cvpartition` | Create a partition object for cross validation.
`@cvpartition/display` | Display a 'cvpartition' object, C.
`@cvpartition/get` | Get a field, F, from a 'cvpartition' object, C.
`@cvpartition/repartition` | Return a new cvpartition object.
`@cvpartition/set` | Set FIELD, in a 'cvpartition' object, C.
`@cvpartition/test` | Return logical vector for testing-subset indices from a 'cvpartition' object, C.
`@cvpartition/training` | Return logical vector for training-subset indices from a 'cvpartition' object, C.

### Regression Classes

`RegressionGAM` | Create a RegressionGAM class object containing a Generalised Additive Model (GAM) for regression.

### Data Manipulation

`combnk` | Return all combinations of K elements in DATA.
`crosstab` | Create a cross-tabulation (contingency table) T from data vectors.
`datasample` | Randomly sample data.
`fillmissing` | Replace missing entries of array A either with values in V or as determined by other specified methods. 'missing' values are determined by the data type of A as identified by the function ismissing, curently defined as:
`grp2idx` | Get index for group variables.
`ismissing` | Find missing data in a numeric or string array.
`isoutlier` | Find outliers in data
`normalise_distribution` | Transform a set of data so as to be N(0,1) distributed according to an idea by van Albada and Robinson.
`rmmissing` | Remove missing or incomplete data from an array.
`standardizeMissing` | Replace data values specified by INDICATOR in A by the standard 'missing' data value for that data type.
`tabulate` | Calculate a frequency table.

### Descriptive Statistics

`cdfcalc` | Calculate an empirical cumulative distribution function.
`cl_multinom` | Confidence level of multinomial portions.
`geomean` | Compute the geometric mean of X.
`grpstats` | Summary statistics by group. 'grpstats' computes groupwise summary statistics, for data in a matrix X. 'grpstats' treats NaNs as missing values, and removes them.
`harmmean` | Compute the harmonic mean of X.
`jackknife` | Compute jackknife estimates of a parameter taking one or more given samples as parameters.
`mad` | Compute the mean or median absolute deviation (MAD).
`mean` | Compute the mean of the elements of X.
`median` | Compute the median value of the elements of X.
`nanmax` | Find the maximal element while ignoring NaN values.
`nanmin` | Find the minimal element while ignoring NaN values.
`nansum` | Compute the sum while ignoring NaN values.
`std` | Compute the standard deviation of the elements of X.
`trimmean` | Compute the trimmed mean.
`var` | Compute the variance of the elements of X.

### Distribution Fitting

`betafit` | Estimate parameters and confidence intervals for the Beta distribution.
`betalike` | Negative log-likelihood for the Beta distribution.
`binofit` | Estimate parameter and confidence intervals for the binomial distribution.
`binolike` | Negative log-likelihood for the binomial distribution.
`bisafit` | Estimate mean and confidence intervals for the Birnbaum-Saunders distribution.
`bisalike` | Negative log-likelihood for the Birnbaum-Saunders distribution.
`burrfit` | Estimate mean and confidence intervals for the Burr type XII distribution.
`burrlike` | Negative log-likelihood for the Burr type XII distribution.
`evfit` | Estimate parameters and confidence intervals for the extreme value distribution.
`evlike` | Negative log-likelihood for the extreme value distribution.
`expfit` | Estimate mean and confidence intervals for the exponential distribution.
`explike` | Negative log-likelihood for the exponential distribution.
`gamfit` | Estimate parameters and confidence intervals for the Gamma distribution.
`gamlike` | Negative log-likelihood for the Gamma distribution.
`geofit` | Estimate parameter and confidence intervals for the geometric distribution.
`gevfit_lmom` | Find an estimator (PARAMHAT) of the generalized extreme value (GEV) distribution fitting DATA using the method of L-moments.
`gevfit` | Estimate parameters and confidence intervals for the generalized extreme value (GEV) distribution.
`gevlike` | Negative log-likelihood for the generalized extreme value (GEV) distribution.
`gpfit` | Estimate parameters and confidence intervals for the generalized Pareto distribution.
`gplike` | Negative log-likelihood for the generalized Pareto distribution.
`gumbelfit` | Estimate parameters and confidence intervals for Gumbel distribution.
`gumbellike` | Negative log-likelihood for the extreme value distribution.
`hnfit` | Estimate parameters and confidence intervals for the half-normal distribution.
`hnlike` | Negative log-likelihood for the half-normal distribution.
`invgfit` | Estimate mean and confidence intervals for the inverse Gaussian distribution.
`invglike` | Negative log-likelihood for the inverse Gaussian distribution.
`logifit` | Estimate mean and confidence intervals for the logistic distribution.
`logilike` | Negative log-likelihood for the logistic distribution.
`loglfit` | Estimate mean and confidence intervals for the log-logistic distribution.
`logllike` | Negative log-likelihood for the log-logistic distribution.
`lognfit` | Estimate parameters and confidence intervals for the log-normal distribution.
`lognlike` | Negative log-likelihood for the log-normal distribution.
`nakafit` | Estimate mean and confidence intervals for the Nakagami distribution.
`nakalike` | Negative log-likelihood for the Nakagami distribution.
`nbinfit` | Estimate parameter and confidence intervals for the negative binomial distribution.
`nbinlike` | Negative log-likelihood for the negative binomial distribution.
`normfit` | Estimate parameters and confidence intervals for the normal distribution.
`normlike` | Negative log-likelihood for the normal distribution.
`poissfit` | Estimate parameter and confidence intervals for the Poisson distribution.
`poisslike` | Negative log-likelihood for the Poisson distribution.
`raylfit` | Estimate parameter and confidence intervals for the Rayleigh distribution.
`rayllike` | Negative log-likelihood for the Rayleigh distribution.
`ricefit` | Estimate parameters and confidence intervals for the Gamma distribution.
`ricelike` | Negative log-likelihood for the Rician distribution.
`unidfit` | Estimate parameter and confidence intervals for the discrete uniform distribution.
`unifit` | Estimate parameter and confidence intervals for the continuous uniform distribution.
`wblfit` | Estimate mean and confidence intervals for the Weibull distribution.
`wbllike` | Negative log-likelihood for the Weibull distribution.

### Distribution Functions

`betacdf` | Beta cumulative distribution function (CDF).
`betainv` | Inverse of the Beta distribution (iCDF).
`betapdf` | Beta probability density function (PDF).
`betarnd` | Random arrays from the Beta distribution.
`binocdf` | Binomial cumulative distribution function (CDF).
`binoinv` | Inverse of the Binomial cumulative distribution function (iCDF).
`binopdf` | Binomial probability density function (PDF).
`binornd` | Random arrays from the Binomial distribution.
`bisacdf` | Birnbaum-Saunders cumulative distribution function (CDF).
`bisainv` | Inverse of the Birnbaum-Saunders cumulative distribution function (iCDF).
`bisapdf` | Birnbaum-Saunders probability density function (PDF).
`bisarnd` | Random arrays from the Birnbaum-Saunders distribution.
`burrcdf` | Burr type XII cumulative distribution function (CDF).
`burrinv` | Inverse of the Burr type XII cumulative distribution function (iCDF).
`burrpdf` | Burr type XII probability density function (PDF).
`burrrnd` | Random arrays from the Burr type XII distribution.
`bvncdf` | Bivariate normal cumulative distribution function (CDF).
`bvtcdf` | Bivariate Student's t cumulative distribution function (CDF).
`cauchycdf` | Cauchy cumulative distribution function (CDF).
`cauchyinv` | Inverse of the Cauchy cumulative distribution function (iCDF).
`cauchypdf` | Cauchy probability density function (PDF).
`cauchyrnd` | Random arrays from the Cauchy distribution.
`chi2cdf` | Chi-squared cumulative distribution function (CDF).
`chi2inv` | Inverse of the chi-squared cumulative distribution function (iCDF).
`chi2pdf` | Chi-squared probability density function (PDF).
`chi2rnd` | Random arrays from the chi-squared distribution.
`copulacdf` | Copula family cumulative distribution functions (CDF).
`copulapdf` | Copula family probability density functions (PDF).
`copularnd` | Random arrays from the copula family distributions.
`evcdf` | Extreme value cumulative distribution function (CDF).
`evinv` | Inverse of the extreme value cumulative distribution function (iCDF).
`evpdf` | Extreme value probability density function (PDF).
`evrnd` | Random arrays from the extreme value distribution.
`expcdf` | Exponential cumulative distribution function (CDF).
`expinv` | Inverse of the exponential cumulative distribution function (iCDF).
`exppdf` | Exponential probability density function (PDF).
`exprnd` | Random arrays from the exponential distribution.
`fcdf` | F-cumulative distribution function (CDF).
`finv` | Inverse of the F-cumulative distribution function (iCDF).
`fpdf` | F-probability density function (PDF).
`frnd` | Random arrays from the F-distribution.
`gamcdf` | Gamma cumulative distribution function (CDF).
`gaminv` | Inverse of the Gamma cumulative distribution function (iCDF).
`gampdf` | Gamma probability density function (PDF).
`gamrnd` | Random arrays from the Gamma distribution.
`geocdf` | Geometric cumulative distribution function (CDF).
`geoinv` | Inverse of the geometric cumulative distribution function (iCDF).
`geopdf` | Geometric probability density function (PDF).
`geornd` | Random arrays from the geometric distribution.
`gevcdf` | Generalized extreme value (GEV) cumulative distribution function (CDF).
`gevinv` | Inverse of the generalized extreme value (GEV) cumulative distribution function (iCDF).
`gevpdf` | Generalized extreme value (GEV) probability density function (PDF).
`gevrnd` | Random arrays from the generalized extreme value (GEV) distribution.
`gpcdf` | Generalized Pareto cumulative distribution function (CDF).
`gpinv` | Inverse of the generalized Pareto cumulative distribution function (iCDF).
`gppdf` | Generalized Pareto probability density function (PDF).
`gprnd` | Random arrays from the generalized Pareto distribution.
`gumbelcdf` | Gumbel cumulative distribution function (CDF).
`gumbelinv` | Inverse of the Gumbel cumulative distribution function (iCDF).
`gumbelpdf` | Gumbel probability density function (PDF).
`gumbelrnd` | Random arrays from the Gumbel distribution.
`hncdf` | Half-normal cumulative distribution function (CDF).
`hninv` | Inverse of the half-normal cumulative distribution function (iCDF).
`hnpdf` | Half-normal probability density function (PDF).
`hnrnd` | Random arrays from the half-normal distribution.
`hygecdf` | Hypergeometric cumulative distribution function (CDF).
`hygeinv` | Inverse of the hypergeometric cumulative distribution function (iCDF).
`hygepdf` | Hypergeometric probability density function (PDF).
`hygernd` | Random arrays from the hypergeometric distribution.
`invgcdf` | Inverse Gaussian cumulative distribution function (CDF).
`invginv` | Inverse of the inverse Gaussian cumulative distribution function (iCDF).
`invgpdf` | Inverse Gaussian probability density function (PDF).
`invgrnd` | Random arrays from the inverse Gaussian distribution.
`iwishpdf` | Compute the probability density function of the inverse Wishart distribution.
`iwishrnd` | Return a random matrix sampled from the inverse Wishart distribution with given parameters.
`jsucdf` | Johnson SU cumulative distribution function (CDF).
`jsupdf` | Johnson SU probability density function (PDF).
`laplacecdf` | Laplace cumulative distribution function (CDF).
`laplaceinv` | Inverse of the Laplace cumulative distribution function (iCDF).
`laplacepdf` | Laplace probability density function (PDF).
`laplacernd` | Random arrays from the Laplace distribution.
`logicdf` | Logistic cumulative distribution function (CDF).
`logiinv` | Inverse of the logistic cumulative distribution function (iCDF).
`logipdf` | Logistic probability density function (PDF).
`logirnd` | Random arrays from the logistic distribution.
`loglcdf` | Log-logistic cumulative distribution function (CDF).
`loglinv` | Inverse of the log-logistic cumulative distribution function (iCDF).
`loglpdf` | Log-logistic probability density function (PDF).
`loglrnd` | Random arrays from the log-logistic distribution.
`logncdf` | Log-normal cumulative distribution function (CDF).
`logninv` | Inverse of the log-normal cumulative distribution function (iCDF).
`lognpdf` | Lognormal probability density function (PDF).
`lognrnd` | Random arrays from the lognormal distribution.
`mnpdf` | Multinomial probability density function (PDF).
`mnrnd` | Random arrays from the multinomial distribution.
`mvncdf` | Multivariate normal cumulative distribution function (CDF).
`mvnpdf` | Multivariate normal probability density function (PDF).
`mvnrnd` | Random vectors from the multivariate normal distribution.
`mvtcdf` | Multivariate Student's t cumulative distribution function (CDF).
`mvtpdf` | Multivariate Student's t probability density function (PDF).
`mvtrnd` | Random vectors from the multivariate Student's t distribution.
`mvtcdfqmc` | Quasi-Monte-Carlo computation of the multivariate Student's T CDF.
`nakacdf` | Nakagami cumulative distribution function (CDF).
`nakainv` | Inverse of the Nakagami cumulative distribution function (iCDF).
`nakapdf` | Nakagami probability density function (PDF).
`nakarnd` | Random arrays from the Nakagami distribution.
`nbincdf` | Negative binomial cumulative distribution function (CDF).
`nbininv` | Inverse of the negative binomial cumulative distribution function (iCDF).
`nbinpdf` | Negative binomial probability density function (PDF).
`nbinrnd` | Random arrays from the negative binomial distribution.
`ncfcdf` | Noncentral F-cumulative distribution function (CDF).
`ncfinv` | Inverse of the noncentral F-cumulative distribution function (iCDF).
`ncfpdf` | Noncentral F-probability density function (PDF).
`ncfrnd` | Random arrays from the noncentral F-distribution.
`nctcdf` | Noncentral t-cumulative distribution function (CDF).
`nctinv` | Inverse of the non-central t-cumulative distribution function (iCDF).
`nctpdf` | Noncentral t-probability density function (PDF).
`nctrnd` | Random arrays from the noncentral t-distribution.
`ncx2cdf` | Noncentral chi-squared cumulative distribution function (CDF).
`ncx2inv` | Inverse of the noncentral chi-squared cumulative distribution function (iCDF).
`ncx2pdf` | Noncentral chi-squared probability distribution function (PDF).
`ncx2rnd` | Random arrays from the noncentral chi-squared distribution.
`normcdf` | Normal cumulative distribution function (CDF).
`norminv` | Inverse of the normal cumulative distribution function (iCDF).
`normpdf` | Normal probability density function (PDF).
`normrnd` | Random arrays from the normal distribution.
`poisscdf` | Poisson cumulative distribution function (CDF).
`poissinv` | Inverse of the Poisson cumulative distribution function (iCDF).
`poisspdf` | Poisson probability density function (PDF).
`poissrnd` | Random arrays from the Poisson distribution.
`raylcdf` | Rayleigh cumulative distribution function (CDF).
`raylinv` | Inverse of the Rayleigh cumulative distribution function (iCDF).
`raylpdf` | Rayleigh probability density function (PDF).
`raylrnd` | Random arrays from the Rayleigh distribution.
`ricecdf` | Rician cumulative distribution function (CDF).
`riceinv` | Inverse of the Rician distribution (iCDF).
`ricepdf` | Rician probability density function (PDF).
`ricernd` | Random arrays from the Rician distribution.
`tcdf` | Student's T cumulative distribution function (CDF).
`tinv` | Inverse of the Student's T cumulative distribution function (iCDF).
`tpdf` | Student's T probability density function (PDF).
`trnd` | Random arrays from the Student's T distribution.
`tlscdf` | Location-scale Student's T cumulative distribution function (CDF).
`tlsinv` | Inverse of the location-scale Student's T cumulative distribution function (iCDF).
`tlspdf` | Location-scale Student's T probability density function (PDF).
`tlsrnd` | Random arrays from the location-scale Student's T distribution.
`tricdf` | Triangular cumulative distribution function (CDF).
`triinv` | Inverse of the triangular cumulative distribution function (iCDF).
`tripdf` | Triangular probability density function (PDF).
`trirnd` | Random arrays from the triangular distribution.
`unidcdf` | Discrete uniform cumulative distribution function (CDF).
`unidinv` | Inverse of the discrete uniform cumulative distribution function (iCDF).
`unidpdf` | Discrete uniform probability density function (PDF).
`unidrnd` | Random arrays from the discrete uniform distribution.
`unifcdf` | Continuous uniform cumulative distribution function (CDF).
`unifinv` | Inverse of the continuous uniform cumulative distribution function (iCDF).
`unifpdf` | Continuous uniform probability density function (PDF).
`unifrnd` | Random arrays from the continuous uniform distribution.
`vmcdf` | Von Mises probability density function (PDF).
`vminv` | Inverse of the von Mises cumulative distribution function (iCDF).
`vmpdf` | Von Mises probability density function (PDF).
`vmrnd` | Random arrays from the von Mises distribution.
`wblcdf` | Weibull cumulative distribution function (CDF).
`wblinv` | Inverse of the Weibull cumulative distribution function (iCDF).
`wblpdf` | Weibull probability density function (PDF).
`wblrnd` | Random arrays from the Weibull distribution.
`wienrnd` | Return a simulated realization of the D-dimensional Wiener Process on the interval [0, T].
`wishpdf` | Compute the probability density function of the Wishart distribution
`wishrnd` | Return a random matrix sampled from the Wishart distribution with given parameters

### Distribution Statistics

`betastat` | Compute statistics of the Beta distribution.
`binostat` | Compute statistics of the binomial distribution.
`chi2stat` | Compute statistics of the chi-squared distribution.
`evstat` | Compute statistics of the extreme value distribution.
`expstat` | Compute statistics of the exponential distribution.
`fstat` | Compute statistics of the F-distribution.
`gamstat` | Compute statistics of the Gamma distribution.
`geostat` | Compute statistics of the geometric distribution.
`gevstat` | Compute statistics of the generalized extreme value distribution.
`gpstat` | Compute statistics of the generalized Pareto distribution.
`hygestat` | Compute statistics of the hypergeometric distribution.
`lognstat` | Compute statistics of the log-normal distribution.
`nbinstat` | Compute statistics of the negative binomial distribution.
`ncfstat` | Compute statistics for the noncentral F-distribution.
`nctstat` | Compute statistics for the noncentral t-distribution.
`ncx2stat` | Compute statistics for the noncentral chi-squared distribution.
`normstat` | Compute statistics of the normal distribution.
`poisstat` | Compute statistics of the Poisson distribution.
`raylstat` | Compute statistics of the Rayleigh distribution.
`ricestat` | Compute statistics of the Rician distribution.
`tlsstat` | Compute statistics of the location-scale Student's T distribution.
`tstat` | Compute statistics of the Student's T distribution.
`unidstat` | Compute statistics of the discrete uniform cumulative distribution.
`unifstat` | Compute statistics of the continuous uniform cumulative distribution.
`wblstat` | Compute statistics of the Weibull distribution.

### Experimental Design

`fullfact` | Full factorial design.
`ff2n` | Two-level full factorial design.
`sigma_pts` | Calculates 2*N+1 sigma points in N dimensions.
`x2fx` | Convert predictors to design matrix.

### Machine Learning

`hmmestimate` | Estimation of a hidden Markov model for a given sequence.
`hmmgenerate` | Output sequence and hidden states of a hidden Markov model.
`hmmviterbi` | Viterbi path of a hidden Markov model.
`svmpredict` | This function predicts new labels from a testing instance matrtix based on an SVM MODEL created with 'svmtrain'.
`svmtrain` | This function trains an SVM MODEL based on known LABELS and their corresponding DATA which comprise an instance matrtix.

### Model Fitting

`fitcknn` | Fit a k-Nearest Neighbor classification model.
`fitgmdist` | Fit a Gaussian mixture model with K components to DATA.
`fitlm` | 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.
`fitrgam` | Fit a Generalised Additive Model (GAM) for regression.

### Hypothesis Testing

`adtest` | Anderson-Darling goodness-of-fit hypothesis test.
`anova1` | Perform a one-way analysis of variance (ANOVA) for comparing the means of two or more groups of data under the null hypothesis that the groups are drawn from distributions with the same mean.
`anova2` | Performs two-way factorial (crossed) or a nested analysis of variance (ANOVA) for balanced designs.
`anovan` | 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).
`bartlett_test` | Perform a Bartlett test for the homogeneity of variances.
`barttest` | Bartlett's test of sphericity for correlation.
`binotest` | Test for probability P of a binomial sample
`chi2gof` | Chi-square goodness-of-fit test.
`chi2test` | Perform a chi-squared test (for independence or homogeneity).
`correlation_test` | Perform a correlation coefficient test whether two samples X and Y come from uncorrelated populations.
`fishertest` | Fisher's exact test.
`friedman` | Performs the nonparametric Friedman's test to compare column effects in a two-way layout. friedman tests the null hypothesis that the column effects are all the same against the alternative that they are not all the same.
`hotelling_t2test` | Compute Hotelling's T^2 ("T-squared") test for a single sample or two dependent samples (paired-samples).
`hotelling_t2test2` | Compute Hotelling's T^2 ("T-squared") test for two independent samples.
`kruskalwallis` | Perform a Kruskal-Wallis test, the non-parametric alternative of a one-way analysis of variance (ANOVA), for comparing the means of two or more groups of data under the null hypothesis that the groups are drawn from the same population, ...
`kstest` | Single sample Kolmogorov-Smirnov (K-S) goodness-of-fit hypothesis test.
`kstest2` | Two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test.
`levene_test` | Perform a Levene's test for the homogeneity of variances.
`manova1` | One-way multivariate analysis of variance (MANOVA).
`mcnemar_test` | Perform a McNemar's test on paired nominal data.
`multcompare` | Perform posthoc multiple comparison tests or p-value adjustments to control the family-wise error rate (FWER) or false discovery rate (FDR).
`ranksum` | Wilcoxon rank sum test for equal medians.
`regression_ftest` | F-test for General Linear Regression Analysis
`regression_ttest` | Perform a linear regression t-test.
`runstest` | Run test for randomness in the vector X.
`sampsizepwr` | Sample size and power calculation for hypothesis test.
`signtest` | Test for median.
`ttest` | Test for mean of a normal sample with unknown variance.
`ttest2` | Perform a t-test to compare the means of two groups of data under the null hypothesis that the groups are drawn from distributions with the same mean.
`vartest` | One-sample test of variance.
`vartest2` | Two-sample F test for equal variances.
`vartestn` | Test for equal variances across multiple groups.
`ztest` | One-sample Z-test.
`ztest2` | Two proportions Z-test.

### I/O

`libsvmread` | This function reads the labels and the corresponding instance_matrix from a LIBSVM data file and stores them in LABELS and DATA respectively.
`libsvmwrite` | This function saves the labels and the corresponding instance_matrix in a file specified by FILENAME.

### Plotting

`boxplot` | Produce a box plot.
`cdfplot` | Display an empirical cumulative distribution function.
`confusionchart` | Display a chart of a confusion matrix.
`dendrogram` | Plot a dendrogram of a hierarchical binary cluster tree.
`ecdf` | Empirical (Kaplan-Meier) cumulative distribution function.
`einstein` | Plots the tiling of the basic clusters of einstein tiles.
`gscatter` | Draw a scatter plot with grouped data.
`histfit` | Plot histogram with superimposed fitted normal density.
`hist3` | Produce bivariate (2D) histogram counts or plots.
`manovacluster` | Cluster group means using manova1 output.
`normplot` | Produce normal probability plot of the data in X.
`ppplot` | Perform a PP-plot (probability plot).
`qqplot` | Perform a QQ-plot (quantile plot).
`silhouette` | Compute the silhouette values of clustered data and show them on a plot.
`violin` | Produce a Violin plot of the data X.
`wblplot` | Plot a column vector DATA on a Weibull probability plot using rank regression.

### Regression

`canoncorr` | Canonical correlation analysis.
`cholcov` | Cholesky-like decomposition for covariance matrix.
`dcov` | Distance correlation, covariance and correlation statistics.
`logistic_regression` | Perform ordinal logistic regression.
`mnrfit` | Perform logistic regression for binomial responses or multiple ordinal responses.
`monotone_smooth` | Produce a smooth monotone increasing approximation to a sampled functional dependence.
`pca` | Performs a principal component analysis on a data matrix.
`pcacov` | Perform principal component analysis on covariance matrix
`pcares` | Calculate residuals from principal component analysis.
`plsregress` | Calculate partial least squares regression using SIMPLS algorithm.
`princomp` | Performs a principal component analysis on a NxP data matrix X.
`regress` | Multiple Linear Regression using Least Squares Fit of Y on X with the model 'y = X * beta + e'.
`regress_gp` | Regression using Gaussian Processes.
`ridge` | Ridge regression.
`stepwisefit` | Linear regression with stepwise variable selection.

### Transforms

`logit` | Compute the logit for each value of P
`probit` | Probit transformation

### Wrappers

`cdf` | Return the CDF of a univariate distribution evaluated at X.
`icdf` | Return the inverse CDF of a univariate distribution evaluated at P.
`pdf` | Return the PDF of a univariate distribution evaluated at X.
`random` | Random arrays from a given one-, two-, or three-parameter distribution.