ALLBEST Procedure—Selects the best multiple linear regression models.

ANCOVAR Function—Analyzes a one-way classification model with covariates.

ANOVA1 Function—Analyzes one-way classification model.

ANOVABALANCED Function—Balanced fixed, random, or mixed model.

ANOVAFACT Function—Analyzes a balanced factorial design with fixed effects.

ANOVANESTED Function—Nested random mode.

ARMA Function—Computes method-of-moments or least-squares estimates of parameters for a nonseasonal ARMA model.

AUTO_ARIMA Function—Automatically identifies time series outliers, determines parameters of a multiplicative seasonal ARIMA model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.

AUTOCORRELATION Function—Sample autocorrelation function.

AUTO_UNI_AR Function—Automatic selection and fitting of a univariate autoregressive time series model.

BETA Function—Evaluate the complete beta function.

BETACDF Function—Evaluates the beta probability distribution function.

BETAI Function—Evaluate the real incomplete beta function.

BINOMIALCDF Function—Evaluates the binomial distribution function.

BINOMIALCOEF Function—Evaluate binomial coefficient.

BINOMIALPDF Function—Evaluates the binomial probability function.

BINORMALCDF Function—Evaluates the bivariate normal distribution function.

BOXCOXTRANS Function—Perform Box-Cox transformation

CAT_GLM Function—Generalized linear models.

CLUSTER_HIERARCHICAL Procedure—Performs a hierarchical cluster analysis given a distance matrix.

CLUSTER_NUMBER Function—Computes cluster membership for a hierarchical cluster tree.

CHI_SQUARED_NORMALITY_TEST Function—Performs a chi-squared test for normality.

CHISQCDF Function—Evaluates the chi-squared distribution function. Using a keyword, the inverse of the chi-squared distribution can be evaluated.

CHISQTEST Function—Performs a chi-squared goodness-of-fit test.

CMAST_ERR_PRINT Function—Set option for error printing.

CMAST_ERR_STOP Function—Set option for error recovery.

CMAST_ERR_TRANS Function—Determines if an Informational Error has occurred.

COCHRANQ Function—Cochran's Q test.

COMPLEMENTARY_F_CDF Function—Evaluates the complement of the F distribution function.

COMPLEMENTARY_T_CDF Function—Evaluates the complement of the Student’s t distribution.

CONT_TABLE Procedure—Sets up a table to generate pseudorandom numbers from a general continuous distribution.

CONTINGENCY Function—Performs a chi-squared analysis of a two-way contingency table.

COVARIANCES Function—Computes the sample variance-covariance or correlation matrix.

CRD_FACTORIAL Function—Analyzes data from balanced and unbalanced completely randomized experiments.

CROSSCORRELATION Function—Computes the sample cross-correlation function of two stationary time series.

CSTRENDS Function—Cox and Stuarts’ sign test for trends in location and dispersion.

DIFFERENCE Function—Differences a seasonal or nonseasonal time series.

DISCR_ANALYSIS Procedure—Perform discriminant function analysis.

DISCR_TABLE Function—Sets up a table to generate pseudorandom numbers from a general discrete distribution.

DISSIMILARITIES Function—Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.

EMPIRICAL_QUANTILES Function—Computes empirical quantiles.

ESTIMATE_MISSING Function—Estimates missing values in a time series.

EXACT_ENUM Function—Exact probabilities in a table; total enumeration.

EXACT_NETWORK Function—Exact probabilities in a table.

FACTOR_ANALYSIS Function—Extracts initial factor-loading estimates in factor analysis.

FAURE_INIT Function—Initializes the structure used for computing a shuffled Faure sequence.

FAURE_NEXT_PT Function—Generates shuffled Faure sequence.

FCDF Function—Evaluates the F distribution function. Using a keyword, the inverse of the F distribution function can be evaluated.

FREQTABLE Function—Tallies observations into a one-way frequency table.

FRIEDMANS_TEST Function—Friedman’s test.

GA_CHROMOSOME Function—Creates a data structure containing unencoded and encoded phenotype information.

GA_DECODE Function—Decodes an individual’s chromosome into its binary, nominal, integer and real phenotypes.

GA_ENCODE Function—Encodes an individual’s binary, nominal, integer and real phenotypes into its chromosome.

GA_GROW_POPULATION Function—Adds the individuals in the array individual to an existing population.

GA_INDIVIDUAL Function—Creates a data structure from user supplied phenotypes.

GA_MERGE_POPULATION Function—Creates a new population by merging two populations with identical chromosome structures.

GA_MUTATE Function—Performs the mutation operation on an individual’s chromosome.

GA_POPULATION Function—Creates a population data structure from user supplied individuals.

GA_RANDOM_POPULATION Function—Creates a population data structure from randomly generated individuals.

GAMMA_ADV Function—Evaluate the real gamma function.

GAMMACDF Function—Evaluates the gamma distribution function.

GAMMAI Function—Evaluate incomplete gamma function.

GARCH Function—Compute estimates of the parameters of a GARCH(p,q) model

GENETIC_ALGORITHM Function—Optimizes a user-defined fitness function using a tailored genetic algorithm.

HOMOGENEITY Function—Conducts Bartlett’s and Levene’s tests of the homogeneity of variance assumption in analysis of variance.

HYPERGEOCDF Function—Evaluates the hypergeometric distribution function.

HYPERGEOPDF Function—Evaluates the hypergeometric probability function.

HYPOTH_PARTIAL Function—Constructs an equivalent completely testable multivariate general linear hypothesis HβU = G from a partially testable hypothesis HpβU = Gp.

HYPOTH_SCPH Function—Computes the matrix of sums of squares and crossproducts for the multivariate general linear hypothesis HβU = G given the regression fit.

HYPOTH_TEST Function—Performs tests for a multivariate general linear hypothesis HβU = G given the hypothesis sums of squares and crossproducts matrix SH.

K_MEANS Function—Performs a K-means (centroid) cluster analysis.

KALMAN Procedure—Performs Kalman filtering and evaluates the likelihood function or the state-space model.

KAPLAN_MEIER_ESTIMATES Function—Computes Kaplan-Meier estimates of survival probabilities in stratified samples.

KOLMOGOROV1 Function—One-sample continuos data Kolmogorov-Smirnov.

KOLMOGOROV2 Function—Two-sample continuos data Kolmogorov-Smirnov.

KTRENDS Function—K-sample trends test.

KW_TEST Function—Kruskal-Wallis test.

LACK_OF_FIT Function—Lack-of-fit test based on the corrleation function

LATIN_SQUARE Function—Analyzes data from latin-square experiments.

LATTICE_DESIGN Function— Analyzes balanced and partially-balanced lattice experiments.

LIFE_TABLES Function—Produces population and cohort life tables.

LILLIEFORS_NORMALITY_TEST Function—Performs a Lilliefors test for normality.

LNBETA Function—Evaluate the log of the real beta function.

LNGAMMA Function—Evaluate the logarithm of the absolute value of the gamma function.

LNORMREGRESS Function—Fits a multiple linear regression model using criteria other than least squares. Namely, LNORMREGRESS allows the user to choose Least Absolute Value (L1), Least Lp norm (Lp), or Least Maximum Value (Minimax or L∞) method of multiple linear regression.

MACHINE Function—Returns information describing the computer’s arithmetic.

MAX_ARMA Function—Exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive, moving average) time series model.

MLFF_CLASSIFICATION_TRAINER Function—Trains a multilayered feedforward neural network.

MLFF_INITIALIZE_WEIGHTS Function—Initializes weights for multilayered feedforward neural networks prior to network training using one of four user selected methods.

MLFF_NETWORK Function—Links and modifies a multilayered feedforward neural network.

MLFF_NETWORK_FORECAST Function—Calculates forecasts for trained multilayered feedforward neural networks.

MLFF_NETWORK_INIT Function—Creates a multilayered feedforward neural network.

MLFF_NETWORK_TRAINER Function—Trains a multilayered feedforward neural network using quasi-Newton backpropagation.

MLFF_PATTERN_CLASSIFICATION Function—Trains a multilayered feedforward neural network for classification.

MULTICOMP Function—Performs Student-Newman-Keuls multiple-comparisons test.

MULTI_CROSS Function—Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.

MULTIPREDICT Function—Computes predicted values, confidence intervals, and diagnostics after fitting a regression model.

MULTIREGRESS Function—Fits a multiple linear regression model using least squares and optionally compute summary statistics for the regression model.

MULTIVARIATE_NORMAL_CDF Function—Evaluates the cumulative distribution function for the multivariate normal distribution.

MVAR_NORMALITY Function—Mardia’s test for multivariate normality.

NAIVE_BAYES_CLASSIFICATION Function—Classifies unknown patterns using a previously trained Naive Bayes classifier.

NAIVE_BAYES_TRAINER Function—Trains a Naive Bayes classifier.

NCTRENDS Function—Noehter’s test for cyclical trend.

NON_CENTRAL_F_CDF Function—Evaluates the noncentral F cumulative distribution function (CDF).

NON_CENTRAL_F_PDF Function—Evaluates the noncentral F probability density function (PDF).

NON_CENTRAL_CHI_SQ_PDF Function—Evaluates the noncentral chi-squared probability density function.

NONLINOPT Function—Fits data to a nonlinear model (possibly with linear constraints) using the successive quadratic programming algorithm (applied to the sum of squared errors, sse = Σ(yi − f(xi; θ))2) and either a finite difference gradient or a user-supplied gradient.

NONLINREGRESS Function—Fits a nonlinear regression model.

NONPARAM_HAZARD_RATE Function—Performs nonparametric hazard rate estimation using kernel functions and quasi-likelihoods.

NORM1SAMP Function—Computes statistics for mean and variance inferences using a sample from a normal population.

NORM2SAMP Function—Computes statistics for mean and variance inferences using samples from two independently normal populations.

NORMALCDF Function—Evaluates the standard normal (Gaussian) distribution function. Using a keyword, the inverse of the standard normal (Gaussian) distribution can be evaluated.

NORMALITY Function—Performs a test for normality.

PARTIAL_AC Function—Sample partial autocorrelation function

PARTIAL_COV Function—Partial correlations and covariances.

POISSONCDF Function—Evaluates the Poisson distribution function.

POISSONPDF Function—Evaluates the probability function of a Poisson random variable with parameter theta.

POLYPREDICT Function—Computes predicted values, confidence intervals, and diagnostics after fitting a polynomial regression model.

POLYREGRESS Function—Performs a polynomial least-squares regression.

POOLED_COV Function—Pooled covariance matrix.

PRINC_COMP Function—Computes principal components.

PROP_HAZARDS_GEN_LIN Function—Analyzes survival and reliability data using Cox’s proportional hazards model.

RAND_GEN_CONT Function—Generates pseudorandom numbers from a general continuous distribution.

RAND_GEN_DISCR Function—Generates pseudorandom numbers from a general discrete distribution using an alias method or optionally a table lookup method.

RANDOM Function—Generates pseudorandom numbers. The default distribution is a uniform (0, 1) distribution, but many different distributions can be specified through the use of keywords.

RANDOM_ARMA Function—Generate pseudorandom ARMA process numbers

RAND_FROM_DATA Function—Generates pseudorandom numbers from multivariate distribution determined from a given sample.

RANDOM_NPP Function—Generates pseudorandom numbers from a nonhomogeneous Poisson process.

RANDOM_ORDER Function—Generates pseudorandom order statistics from a standard normal distribution.

RAND_ORTH_MAT Function—Generates a pseudorandom orthogonal matrix or a correlation matrix

RANDOM_SAMPLE Function—Generates a simple pseudorandom sample from a finite population

DISCR_TABLE Function—Sets or retrieves the current table used in either the shuffled or GFSR random number generator

RAND_TABLE_2WAY Function—Generates a pseudorandom two-way table.

RANDOM_MT32_INIT Procedure—Initializes the 32-bit Mersenne Twister generator using an array.

RANDOM_MT64_INIT Procedure—Initializes the 64-bit Mersenne Twister generator using an array.

RANDOMNESS_TEST Function—Runs test, Paris-serial test, d2 test or triplets tests.

RANDOMOPT Procedure—Uses keywords to set or retrieve the random number seed or to select the uniform (0, 1) multiplicative, congruential pseudorandom-number generator.

RANKS Function—Computes the ranks, normal scores, or exponential scores for a vector of observations.

RCBD_FACTORIAL Function—Analyzes data from balanced and unbalanced randomized complete-block experiments.

REGRESSORS Function—Generates regressors for a general linear model.

ROBUST_COV Function—Robust estimate of covariance matrix.

SCALE_FILTER Function—Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.

SEASONAL_FIT Function—Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.

SHAPIRO_WILK_NORMALITY_TEST Function—Performs the Shapiro-Wilk test for normality.

SIGNTEST Function—Performs a sign test.

SIMPLESTAT Function—Computes basic univariate statistics.

SORTDATA Function—Sorts observations by specified keys, with option to tally cases into a multiway frequency table.

SPLIT_PLOT Function—Analyzes a wide variety of split-plot experiments with fixed, mixed or random factors.

SPLIT_SPLIT_PLOT Function—Analyzes data from split-split-plot experiments.

STATDATA Function—Retrieves commonly analyzed data sets.

STEPWISE Procedure—Builds multiple linear regression models using forward, backward, or stepwise selection.

STRIP_PLOT Function—Analyzes data from strip-plot experiments.

STRIP_SPLIT_PLOT Function—Analyzes data from strip-split-plot experiments.

SURVIVAL_GLM Function—Analyzes survival data using a generalized linear model and estimates using various parametric modes.

TCDF Function—Evaluates the Student’s t distribution function.

TIE_STATS Function—Tie statistics.

TIME_SERIES_CLASS_FILTER Function—Converts time series data sorted within nominal classes in decreasing chronological order to a useful format for processing by a neural network.

TIME_SERIES_FILTER Function—Converts time series data to the format required for processing by a neural network.

TS_OUTLIER_FORECAST Function—Computes forecasts, their associated probability limits and y weights for an outlier contaminated time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.

TS_OUTLIER_IDENTIFICATION Function—Detects and determines outliers and simultaneously estimates the model parameters in a time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.

UNSUPERVISED_NOMINAL_FILTER Function—Converts nominal data into a series of binary encoded columns for input to a neural network.

UNSUPERVISED_ORDINAL_FILTER Procedure—Converts ordinal data into proportions.

WILCOXON Function—Performs a Wilcoxon rank sum test.

YATES Function—Estimates missing observations in designed experiments using Yate’s method.