Appendix B, Alphabetical Summary of Functions
A] [B] [C] [D] [E] [F] [G] [H] [I] [K] [L] [M] [N] [O] [P] [R] [S] [T] [U] [V] [W]
Function 
Purpose Statement 
Performs an AndersonDarling test for normality. 

Analyzes a oneway classification model with covariates. 

Analyzes a balanced complete experimental design for a fixed, random, or mixed model. 

Analyzes a balanced factorial design with fixed effects. 

Analyzes a completely nested random model with possibly unequal numbers in the subgroups. 

Analyzes a oneway classification model. 

Computes the frequent itemsets in a transaction set. 

Computes the frequent itemsets in a transaction set using aggregation. 

Computes leastsquare estimates of parameters for an ARMA model. 

Computes forecasts and their associated probability limits for an ARMA model. 

Reads freelyformatted ASCII files. 

Computes the sample autocorrelation function of a stationary time series. 

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. 

Estimates structural breaks in nonstationary univariate time series. 

Automatic selection and fitting of a univariate autoregressive time series model. 
Function 
Purpose Statement 
Decomposes a time series into trend, seasonal, and an error component. 

Evaluates the complete beta function. 

Evaluates the beta probability distribution function. 

Evaluates the real incomplete beta function. 

Evaluates the inverse of the beta distribution function. 

Evaluates the binomial distribution function. 

Evaluates the binomial coefficient. 

Evaluates the binomial probability function. 

Evaluates the bivariate normal distribution function. 

Performs a BoxCox transformation. 
Function 
Purpose Statement 
Given an input array of deviate values, generates a canonical correlation array. 

Analyzes categorical data using logistic, Probit, Poisson, and other generalized linear models. 

Evaluates the chisquared distribution function. 

Evaluates the inverse of the chisquared distribution function. 

Performs a chisquared test for normality. 

Performs a chisquared goodnessoffit test. 

Performs a hierarchical cluster analysis given a distance matrix. 

Performs a Kmeans (centroid) cluster analysis. 

Computes cluster membership for a hierarchical cluster tree. 

Performs a Cochran Q test for related observations. 

Calculates the complement of the chisquared distribution. 

Calculates the complement of the F distribution function. 

Evaluates the complementary noncentral F cumulative distribution function (CDF). 

Calculates the complement of the Student's t distribution function. 

Performs a chisquared analysis of a twoway contingency table. 

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

Computes the sample variancecovariance or correlation matrix. 

Performs the Cox and Stuartâ€™ sign test for trends in location and dispersion. 

Analyzes data from balanced and unbalanced completely randomized experiments. 

Computes the sample crosscorrelation function of two stationary time series. 

Performs a CramervonMises test for normality. 
Function 
Purpose Statement 
Reads columnoriented data from a delimited ASCII file and returns a structure with the number of rows and columns and a double matrix containing the data. 

Retrieves a commonly analyzed data set. 

Generates a decision tree for a single response variable and two or more predictor variables. 

Computes predicted values using a decision tree. 

Frees the memory associated with a decision tree. 

Differences a seasonal or nonseasonal time series. 

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

Evaluates the discrete uniform cumulative distribution function (CDF). 

Evaluates the inverse of the discrete uniform cumulative distribution function (CDF). 

Evaluates the discrete uniform probability density function (PDF). 

Performs discriminant function analysis. 

Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix. 
Function 
Purpose Statement 
Computes empirical quantiles. 

Returns the code corresponding to the error message from the last function called. 

Gets the text of the error message from the last function called. 

Sets various error handling options. 

Gets the type corresponding to the error message from the last function called. 

Estimates missing values in a time series. 

Computes exact probabilities in a twoway contingency table, using the total enumeration method. 

Computes exact probabilities in a twoway contingency table using the network algorithm. 

Evaluates the exponential cumulative distribution function (CDF). 

Evaluates the inverse of the exponential cumulative distribution function (CDF). 

Evaluates the exponential probability density function (PDF). 
Function 
Purpose Statement 
Extracts initial factorloading estimates in factor analysis. 

Calculate the False Discovery Rate (FDR) q‑values corresponding to a set of p‑ values from multiple simultaneous hypothesis tests. 

Computes a shuffled Faure sequence. 

Closes a file opened by imsls_fopen. 

Opens a file using the C runtime library used by the IMSL C Stat Library. 

Frees memory returned from an IMSL C Stat Library function. 

Frees the memory allocated within a frequent itemsets structure. 

Frees the memory allocated within an association rules structure. 

Frees the memory allocated for an Imsls_column_info structure. 

Frees the memory allocated for an Imsls_data_matrix structure. 

Performs Friedmanâ€™s test for a randomized complete block design. 
Function 
Purpose Statement 
Codes and decodes binary information from phenotypes to a chromosome. 

Returns a new copy of an existing chromosome. 

Returns a new copy of an existing individual. 

Returns a new copy of an existing population. 

Copies the contents of one chromosome into another chromosome. 

Copies the contents of one individual into another individual. 

Copies the contents of one population into another population. 

Decodes an individualâ€™s chromosome into its binary, nominal, integer and real phenotypes. 

Encodes an individualâ€™s binary, nominal, integer and real phenotypes into its chromosome. 

Frees memory allocated to an existing individual. 

Frees memory allocated to an existing population. 

Adds individuals to an existing population. 

Creates an Imsls_f_individual data structure from user supplied phenotypes. 

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

Performs the mutation operation on an individualâ€™s chromosome. 

Creates an Imsls_f_population data structure from user supplied individuals. 

Creates an Imsls_f_population data structure from randomly generated individuals. 

Evaluates the real gamma functions. 

Evaluates the gamma distribution function. 

Evaluates the incomplete gamma function. 

Evaluates the inverse of the gamma distribution function. 

Computes estimates of the parameters of a GARCH (p, q) model. 

Evaluates the generalized Gaussian cumulative distribution function (CDF). 

Evaluates the inverse cumulative distribution function (CDF) of the generalized Gaussian distribution. 

Evaluates the generalized Gaussian probability density function (PDF). 

Optimizes a user defined fitness function using a tailored genetic algorithm. 

Evaluates the discrete geometric cumulative distribution function (CDF). 

Evaluates the inverse of the discrete geometric cumulative distribution function (CDF). 

Evaluates the discrete geometric probability density function (PDF). 

Performs stochastic gradient boosting of decision trees. 
Function 
Purpose Statement 
Conducts Bartlettâ€™s and Leveneâ€™s tests of the homogeneity of variance assumption in analysis of variance. 

Calculates parameters and forecasts using the HoltWinters Multiplicative or Additive forecasting method for seasonal data. 

Evaluates the hypergeometric distribution function. 

Evaluates the hypergeometric probability function. 

Constructs a completely testable hypothesis. 

Sums of cross products for a multivariate hypothesis. 

Tests for the multivariate linear hypothesis. 
Function 
Purpose Statement 
Locates and optionally replaces dependent variable missing values with nearest neighbor estimates. 

Initializes the IMSL C Stat Library error handling system. 
Function 
Purpose Statement 
Performs Kalman filtering and evaluates the likelihood function for the statespace model. 

Computes KaplanMeier estimates of survival probabilities in stratified samples. 

Calculates forecasts using a trained Kohonen network. 

Trains a Kohonen network. 

Performs a KolmogorovSmirnovâ€™s onesample test for continuous distributions. 

Performs a KolmogorovSmirnovâ€™s twosample test. 

Performs a KruskalWallisâ€™s test for identical population medians. 

Performs ksample trends test against ordered alternatives. 
Function 
Purpose Statement 
Performs lackoffit test for an univariate time series or transfer function given the appropriate correlation function. 

Analyzes data from latinsquare experiments. 

Analyzes balanced and partiallybalanced lattice experiments. 

Produces population and cohort life tables. 

Performs a Lilliefors test for normality. 

Fits a multiple linear regression model using criteria other than least squares. 

Evaluates the log of the real beta function. 

Evaluates the logarithm of the absolute value of the gamma function. 

Fits a binomial or multinomial logistic regression model using iteratively reweighted least squares. 

Frees the memory associated with a logistic regression model structure. 

Predicts a binomial or multinomial outcome given an estimated model and new values of the independent variables. 

Evaluates the lognormal cumulative distribution function (CDF). 

Evaluates the inverse of the lognormal cumulative distribution function (CDF). 

Evaluates the lognormal probability density function (PDF). 
Function 
Purpose Statement 
Returns information describing the computer's floatingpoint arithmetic. 

Returns integer information describing the computer's arithmetic. 

Computes the transpose of a matrix, a matrixvector product, a matrixmatrix product, a bilinear form, or any triple product. 

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

Calculates maximum likelihood estimates for the parameters of one of several univariate probability distributions. 

Trains a multilayered feedforward neural network for classification. 

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

Creates a multilayered feedforward neural network. 

Calculates forecasts for trained multilayered feedforward neural networks. 

Frees memory allocated for an Imsls_f_NN_Network data structure. 

Initializes a data structure for training a neural network. 

Retrieves a neural network from a file previously saved. 

Trains a multilayered feedforward neural network. 

Writes a trained neural network to an ASCII file. 

Calculates classifications for trained multilayered feedforward neural networks. 

Calculates the Area Under the Curve (AUC) or its multiclass analog for classification problems. 

Computes the multichannel crosscorrelation function of two mutually stationary multichannel time series. 

Performs StudentNewmanKeuls multiple comparisons test. 

Computes Mardiaâ€™s multivariate measures of skewness and kurtosis and tests for multivariate normality. 

Computes the cumulative distribution function for the multivariate normal distribution. 
Function 
Purpose Statement 
Classifies unknown patterns using a previously trained NaĂ¯ve Bayes classifier. 

Trains a NaĂ¯ve Bayes classifier. 

Frees memory allocated to an Imsls_f_nb_classifier data structure. 

Retrieves a Naive Bayes Classifier previously filed using imsls_f_nb_clssifier_write. 

Writes a Naive Bayes Classifier to an ASCII file for later retrieval using imsls_f_nb_classifier_read. 

Performs a Noetherâ€™s test for cyclical trend. 

Evaluates the noncentral beta cumulative distribution function (CDF). 

Evaluates the inverse of the noncentral beta cumulative distribution function (CDF). 

Evaluates the noncentral beta probability density function (PDF). 

Evaluates the noncentral chisquared distribution function. 

Evaluates the inverse of the noncentral chisquared function. 

Evaluates the noncentral chisquared probability density function. 

Evaluates the noncentral F cumulative distribution function. 

Evaluates the inverse of the noncentral F cumulative distribution function. 

Evaluates the noncentral F probability density function. 

Evaluates the noncentral Studentâ€™s t distribution function. 

Evaluates the inverse of the noncentral Studentâ€™s t distribution function. 

Evaluates the noncentral Student's t probability density function. 

Fits a nonlinear regression model using Powell's algorithm. 

Fits a nonlinear regression model. 

Performs nonparametric hazard rate estimation using kernel functions and quasilikelihoods. 

Evaluates the standard normal (Gaussian) distribution function. 

Evaluates the inverse of the standard normal (Gaussian) distribution function. 

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

Computes statistics for mean and variance inferences using samples from two normal populations. 
Function 
Purpose Statement 
Sets various OpenMP options. 

Sets the output file or the error message output file. 
Function 
Purpose Statement 
Sets or retrieves the page width or length. 

Evaluates the Pareto cumulative probability distribution function. 

Evaluates the Pareto probability density function. 

Computes the sample partial autocorrelation function of a stationary time series. 

Computes partial covariances or partial correlations from the covariance or correlation matrix. 

Permutes the rows or columns of a matrix. 

Rearranges the elements of a vector as specified by a permutation. 

Performs partial least squares regression for one or more response variables and one or more predictor variables. 

Evaluates the Poisson distribution function. 

Evaluates the Poisson probability function. 

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

Performs a polynomial leastsquares regression. 

Computes a pooled variancecovariance from the observations. 

Computes principal components. 

Analyzes time event data via the proportional hazards model. 
Function 
Purpose Statement 
Generates pseudorandom ARMA process numbers. 

Generates pseudorandom numbers from a beta distribution. 

Generates pseudorandom binomial numbers. 

Generates pseudorandom numbers from a Cauchy distribution. 

Generates pseudorandom numbers from a chisquared distribution. 

Generates pseudorandom numbers from a standard exponential distribution. 

Generates pseudorandom mixed numbers from a standard exponential distribution. 

Generates pseudorandom numbers from a standard gamma distribution. 

Generates pseudorandom numbers from a general continuous distribution. 

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

Generates pseudorandom numbers from a generalized Gaussian distribution. 

Generates pseudorandom numbers from a geometric distribution. 

Retrieves the current table used in the GFSR generator. 

Sets the current table used in the GFSR generator. 

Generates pseudorandom numbers from a hypergeometric distribution. 

Generates pseudorandom numbers from a logarithmic distribution. 

Generates pseudorandom numbers from a lognormal distribution. 

Initializes the 32bit Mersenne Twister generator using an array. 

Retrieves the current table used in the 32bit Mersenne Twister generator. 

Sets the current table used in the 32bit Mersenne Twister generator. 

Initializes the 64bit Mersenne Twister generator using an array. 

Retrieves the current table used in the 64bit Mersenne Twister generator. 

Sets the current table used in the 64bit Mersenne Twister generator. 

Generates pseudorandom numbers from a multinomial distribution. 

Generates pseudorandom numbers from a multivariate distribution determined from a given sample. 

Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Gaussian Copula distribution. 

Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Studentâ€™s t Copula distribution. 

Generates pseudorandom numbers from a negative binomial distribution. 

Generates pseudorandom numbers from a normal, N (Î¼, Ïƒ2), distribution. 

Generates pseudorandom numbers from a multivariate normal distribution. 

Generates pseudorandom numbers from a nonhomogeneous Poisson process. 

Selects the uniform (0, 1) multiplicative congruential pseudorandom number generator. 

Retrieves the uniform (0, 1) multiplicative congruential pseudorandom number generator. 

Generates pseudorandom order statistics from a standard normal distribution. 

Generates pseudorandom order statistics from a uniform (0, 1) distribution. 

Generates a pseudorandom orthogonal matrix or a correlation matrix. 

Generates a pseudorandom permutation. 

Generates pseudorandom numbers from a Poisson distribution. 

Generates a simple pseudorandom sample from a finite population. 

Generates a simple pseudorandom sample of indices. 

Retrieves the current value of the seed used in the IMSL random number generators. 

Initializes a random seed for use in the IMSL random number generators. 

Generates pseudorandom points on a unit circle or Kdimensional sphere. 

Generates pseudorandom numbers from a stable distribution. 

Generates pseudorandom Student's t. 

Retrieves a seed for the congruential generators that do not do shuffling that will generate random numbers beginning 100,000 numbers farther along. 

Retrieves the current table used in the shuffled generator. 

Sets the current table used in the shuffled generator. 

Generates a pseudorandom twoway table. 

Generates pseudorandom numbers from a triangular distribution. 

Generates pseudorandom numbers from a uniform (0, 1) distribution. 

Generates pseudorandom numbers from a discrete uniform distribution. 

Generates pseudorandom numbers from a von Mises distribution. 

Generates pseudorandom numbers from a Weibull distribution. 

Performs a test for randomness. 

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

Analyzes data from balanced and unbalanced randomized completeblock experiments. 

Fits a multiple linear regression model using least squares. 

Fits a univariate, nonseasonal ARIMA time series model with the inclusion of one or more regression variables. 

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

Selects the best multiple linear regression models. 

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

Produces summary statistics for a regression model given the information from the fit. 

Generates regressors for a general linear model. 

Computes a robust estimate of a covariance matrix and mean vector. 
Function 
Purpose Statement 
Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting. 

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

Indicates a condition has occurred in a usersupplied function necessitating a return to the calling function. 

Performs the ShapiroWilk test for normality. 

Performs a sign test. 

Computes basic univariate statistics. 

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

Analyzes a wide variety of splitplot experiments with fixed, mixed or random factors. 

Analyzes data from splitsplitplot experiments. 

Analyzes data from stripplot experiments. 

Analyzes data from stripsplitplot experiments. 

Trains a Support Vector Machines classifier 

Classifies patterns using a previously trained Support Vector Machines classifier 

Estimates using various parametric models. 

Analyzes survival data using a generalized linear model. 

Frees memory allocated for a Support Vector Machines classifier 
Function 
Purpose Statement 
Evaluates the Student's t distribution function. 

Evaluates the inverse of the Student's t distribution function. 

Tallies observations into oneway frequency table. 

Tallies observations into a twoway frequency table. 

Computes tie statistics for a sample of observations. 

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

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

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

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

Converts ordinal data into percentages. 
Function 
Purpose Statement 
Returns integer information describing the version of the library, license number, operating system, and compiler. 

Estimates a vector autoregressive time series model with optional moving average components. 
Function 
Purpose Statement 
Performs a Wilcoxon rank sum test. 

Performs a Wilcoxon sign rank test. 

Prints frequent itemsets. 

Prints association rules. 

Prints a rectangular matrix (or vector) stored in contiguous memory locations. 

Sets or retrieves an option for printing a matrix. 