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
ad_normality_test	Performs an Anderson-Darling test for normality.
ancovar	Analyzes a one-way classification model with covariates.
anova_balanced	Analyzes a balanced complete experimental design for a fixed, random, or mixed model.
anova_factorial	Analyzes a balanced factorial design with fixed effects.
anova_nested	Analyzes a completely nested random model with possibly unequal numbers in the subgroups.
anova_oneway	Analyzes a one-way classification model.
apriori	Computes the frequent itemsets in a transaction set.
aggr_apriori	Computes the frequent itemsets in a transaction set using aggregation.
arma	Computes least-square estimates of parameters for an ARMA model.
arma_forecast	Computes forecasts and their associated probability limits for an ARMA model.
ascii_read	Reads freely-formatted ASCII files.
autocorrelation	Computes the sample autocorrelation function of a stationary time series.
auto_arima	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.
auto_parm	Estimates structural breaks in non-stationary univariate time series.
auto_uni_ar	Automatic selection and fitting of a univariate autoregressive time series model.

Function	Purpose Statement
bayesian_seasonal_adj	Decomposes a time series into trend, seasonal, and an error component.
beta	Evaluates the complete beta function.
beta_cdf	Evaluates the beta probability distribution function.
beta_incomplete	Evaluates the real incomplete beta function.
beta_inverse_cdf	Evaluates the inverse of the beta distribution function.
binomial_cdf	Evaluates the binomial distribution function.
binomial_coefficient	Evaluates the binomial coefficient.
binomial_pdf	Evaluates the binomial probability function.
bivariate_normal_cdf	Evaluates the bivariate normal distribution function.
box_cox_transform	Performs a Box-Cox transformation.

Function	Purpose Statement
canonical_correlation	Given an input array of deviate values, generates a canonical correlation array.
categorical_glm	Analyzes categorical data using logistic, Probit, Poisson, and other generalized linear models.
chi_squared_cdf	Evaluates the chi-squared distribution function.
chi_squared_inverse_cdf	Evaluates the inverse of the chi-squared distribution function.
chi_squared_normality_test	Performs a chi-squared test for normality.
chi_squared_test	Performs a chi-squared goodness-of-fit test.
cluster_hierarchical	Performs a hierarchical cluster analysis given a distance matrix.
cluster_k_means	Performs a K-means (centroid) cluster analysis.
cluster_number	Computes cluster membership for a hierarchical cluster tree.
cochran_q_test	Performs a Cochran Q test for related observations.
complementary_chi_squared_cdf	Calculates the complement of the chi-squared distribution.
complementary_F_cdf	Calculates the complement of the F distribution function.
complementary_non_central_F_cdf	Evaluates the complementary noncentral F cumulative distribution function (CDF).
complementary_t_cdf	Calculates the complement of the Student's t distribution function.
contingency_table	Performs a chi-squared analysis of a two-way contingency table.
continuous_table_setup	Sets up a table to generate pseudorandom numbers from a general continuous distribution.
covariances	Computes the sample variance-covariance or correlation matrix.
cox_stuart_trends_test	Performs the Cox and Stuart’ sign test for trends in location and dispersion.
crd_factorial	Analyzes data from balanced and unbalanced completely randomized experiments.
crosscorrelation	Computes the sample cross-correlation function of two stationary time series.
cvm_normality_test	Performs a Cramer-von-Mises test for normality.

Function	Purpose Statement
data_read	Reads column-oriented data from a delimited ASCII file and returns a structure with the number of rows and columns and a double matrix containing the data.
data_sets	Retrieves a commonly analyzed data set.
decision_tree	Generates a decision tree for a single response variable and two or more predictor variables.
decision_tree_predict	Computes predicted values using a decision tree.
decision_tree_print
decision_tree_free	Frees the memory associated with a decision tree.
difference	Differences a seasonal or nonseasonal time series.
discrete_table_setup	Sets up a table to generate pseudorandom numbers from a general discrete distribution.
discrete_uniform_cdf	Evaluates the discrete uniform cumulative distribution function (CDF).
discrete_uniform_inverse_cdf	Evaluates the inverse of the discrete uniform cumulative distribution function (CDF).
discrete_uniform_pdf	Evaluates the discrete uniform probability density function (PDF).
discriminant_analysis	Performs discriminant function analysis.
dissimilarities	Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.

Function	Purpose Statement
empirical_quantiles	Computes empirical quantiles.
error_code	Returns the code corresponding to the error message from the last function called.
error_message	Gets the text of the error message from the last function called.
error_options	Sets various error handling options.
error_type	Gets the type corresponding to the error message from the last function called.
estimate_missing	Estimates missing values in a time series.
exact_enumeration	Computes exact probabilities in a two-way contingency table, using the total enumeration method.
exact_network	Computes exact probabilities in a two-way contingency table using the network algorithm.
exponential_cdf	Evaluates the exponential cumulative distribution function (CDF).
exponential_inverse_cdf	Evaluates the inverse of the exponential cumulative distribution function (CDF).
exponential_pdf	Evaluates the exponential probability density function (PDF).

Function	Purpose Statement
factor_analysis	Extracts initial factor-loading estimates in factor analysis.
false_discovery_rates	Calculate the False Discovery Rate (FDR) q‑values corresponding to a set of p‑ values from multiple simultaneous hypothesis tests.
faure_next_point	Computes a shuffled Faure sequence.
fclose	Closes a file opened by imsls_fopen.
fopen	Opens a file using the C runtime library used by the IMSL C Stat Library.
free	Frees memory returned from an IMSL C Stat Library function.
free_apriori_itemsets	Frees the memory allocated within a frequent itemsets structure.
free_association_rules	Frees the memory allocated within an association rules structure.
free_column_info	Frees the memory allocated for an Imsls_column_info structure.
free_data_matrix	Frees the memory allocated for an Imsls_data_matrix structure.
friedmans_test	Performs Friedman’s test for a randomized complete block design.

Function	Purpose Statement
ga_chromosome	Codes and decodes binary information from phenotypes to a chromosome.
ga_clone_chromosome	Returns a new copy of an existing chromosome.
ga_clone_individual	Returns a new copy of an existing individual.
ga_clone_population	Returns a new copy of an existing population.
ga_copy_chromosome	Copies the contents of one chromosome into another chromosome.
ga_copy_individual	Copies the contents of one individual into another individual.
ga_copy_population	Copies the contents of one population into another population.
ga_decode	Decodes an individual’s chromosome into its binary, nominal, integer and real phenotypes.
ga_encode	Encodes an individual’s binary, nominal, integer and real phenotypes into its chromosome.
ga_free_individual	Frees memory allocated to an existing individual.
ga_free_population	Frees memory allocated to an existing population.
ga_grow_population	Adds individuals to an existing population.
ga_individual	Creates an Imsls_f_individual data structure from user supplied phenotypes.
ga_merge_population	Creates a new population by merging two populations with identical chromosome structures.
ga_mutate	Performs the mutation operation on an individual’s chromosome.
ga_population	Creates an Imsls_f_population data structure from user supplied individuals.
ga_random_population	Creates an Imsls_f_population data structure from randomly generated individuals.
gamma	Evaluates the real gamma functions.
gamma_cdf	Evaluates the gamma distribution function.
gamma_incomplete	Evaluates the incomplete gamma function.
gamma_inverse_cdf	Evaluates the inverse of the gamma distribution function.
garch	Computes estimates of the parameters of a GARCH (p, q) model.
generalized_gaussian_cdf	Evaluates the generalized Gaussian cumulative distribution function (CDF).
generalized_gaussian_inverse_cdf	Evaluates the inverse cumulative distribution function (CDF) of the generalized Gaussian distribution.
generalized_gaussian_pdf	Evaluates the generalized Gaussian probability density function (PDF).
genetic_algorithm	Optimizes a user defined fitness function using a tailored genetic algorithm.
geometric_cdf	Evaluates the discrete geometric cumulative distribution function (CDF).
geometric_inverse_cdf	Evaluates the inverse of the discrete geometric cumulative distribution function (CDF).
geometric_pdf	Evaluates the discrete geometric probability density function (PDF).
gradient_boosting	Performs stochastic gradient boosting of decision trees.
gradient_boosting_predict	Uses a previously trained gradient boosting model to predict a univariate response variable based on new data.
gradient_boosting_model_free	Frees the memory associated with a gradient boosting model structure.
gradient_boosting_model_read	Retrieves a gradient boosting model previously filed using imsls_f_gradient_boosting_model_write.
gradient_boosting_model_write	Writes a gradient boosting model to an ASCII file for later retrieval using imsls_f_gradient_boosting_model_read.

Function	Purpose Statement
homogeneity	Conducts Bartlett’s and Levene’s tests of the homogeneity of variance assumption in analysis of variance.
hw_time_series	Calculates parameters and forecasts using the Holt-Winters Multiplicative or Additive forecasting method for seasonal data.
hypergeometric_cdf	Evaluates the hypergeometric distribution function.
hypergeometric_pdf	Evaluates the hypergeometric probability function.
hypothesis_partial	Constructs a completely testable hypothesis.
hypothesis_scph	Sums of cross products for a multivariate hypothesis.
hypothesis_test	Tests for the multivariate linear hypothesis.

Function	Purpose Statement
impute_missing	Locates and optionally replaces dependent variable missing values with nearest neighbor estimates.
initialize_error_handler	Initializes the IMSL C Stat Library error handling system.

Function	Purpose Statement
kalman	Performs Kalman filtering and evaluates the likelihood function for the state-space model.
kaplan_meier_estimates	Computes Kaplan-Meier estimates of survival probabilities in stratified samples.
kohonenSOM_forecast	Calculates forecasts using a trained Kohonen network.
kohonenSOM_trainer	Trains a Kohonen network.
kolmogorov_one	Performs a Kolmogorov-Smirnov’s one-sample test for continuous distributions.
kolmogorov_two	Performs a Kolmogorov-Smirnov’s two-sample test.
kruskal_wallis_test	Performs a Kruskal-Wallis’s test for identical population medians.
k_trends_test	Performs k-sample trends test against ordered alternatives.

Function	Purpose Statement
lack_of_fit	Performs lack-of-fit test for an univariate time series or transfer function given the appropriate correlation function.
latin_square	Analyzes data from latin-square experiments.
lattice	Analyzes balanced and partially-balanced lattice experiments.
life_tables	Produces population and cohort life tables.
lilliefors_normality_test	Performs a Lilliefors test for normality.
Lnorm_regression	Fits a multiple linear regression model using criteria other than least squares.
log_beta	Evaluates the log of the real beta function.
log_gamma	Evaluates the logarithm of the absolute value of the gamma function.
logistic_regression	Fits a binomial or multinomial logistic regression model using iteratively reweighted least squares.
logistic_regression_free	Frees the memory associated with a logistic regression model structure.
logistic_reg_predict	Predicts a binomial or multinomial outcome given an estimated model and new values of the independent variables.
lognormal_cdf	Evaluates the lognormal cumulative distribution function (CDF).
lognormal_inverse_cdf	Evaluates the inverse of the lognormal cumulative distribution function (CDF).
lognormal_pdf	Evaluates the lognormal probability density function (PDF).

Function	Purpose Statement
machine (float)	Returns information describing the computer's floating-point arithmetic.
machine (integer)	Returns integer information describing the computer's arithmetic.
mat_mul_rect	Computes the transpose of a matrix, a matrix-vector product, a matrix-matrix product, a bilinear form, or any triple product.
max_arma	Exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive, moving average) time series model.
max_likelihood_estimates	Calculates maximum likelihood estimates for the parameters of one of several univariate probability distributions.
mlff_classification_trainer	Trains a multilayered feedforward neural network for classification.
mlff_initialize_weights	Initializes weights for multilayered feedforward neural networks prior to network training using one of four user selected methods.
mlff_network	Creates a multilayered feedforward neural network.
mlff_network_forecast	Calculates forecasts for trained multilayered feedforward neural networks.
mlff_network_free	Frees memory allocated for an Imsls_f_NN_Network data structure.
mlff_network_init	Initializes a data structure for training a neural network.
mlff_network_read	Retrieves a neural network from a file previously saved.
mlff_network_trainer	Trains a multilayered feedforward neural network.
mlff_network_write	Writes a trained neural network to an ASCII file.
mlff_pattern_classification	Calculates classifications for trained multilayered feedforward neural networks.
multiclass_auc	Calculates the Area Under the Curve (AUC) or its multiclass analog for classification problems.
multi_crosscorrelation	Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.
multiple_comparisons	Performs Student-Newman-Keuls multiple comparisons test.
multivar_normality_test	Computes Mardia’s multivariate measures of skewness and kurtosis and tests for multivariate normality.
multivariate_normal_cdf	Computes the cumulative distribution function for the multivariate normal distribution.

Function	Purpose Statement
naive_bayes_classification	Classifies unknown patterns using a previously trained Naïve Bayes classifier.
naive_bayes_trainer	Trains a Naïve Bayes classifier.
nb_classifier_free	Frees memory allocated to an Imsls_f_nb_classifier data structure.
nb_classifier_read	Retrieves a Naive Bayes Classifier previously filed using imsls_f_nb_clssifier_write.
nb_classifier_write	Writes a Naive Bayes Classifier to an ASCII file for later retrieval using imsls_f_nb_classifier_read.
noether_cyclical_trend	Performs a Noether’s test for cyclical trend.
non_central_beta_cdf	Evaluates the noncentral beta cumulative distribution function (CDF).
non_central_beta_inverse_cdf	Evaluates the inverse of the noncentral beta cumulative distribution function (CDF).
non_central_beta_pdf	Evaluates the noncentral beta probability density function (PDF).
non_central_chi_sq	Evaluates the noncentral chi-squared distribution function.
non_central_chi_sq_inv	Evaluates the inverse of the noncentral chi-squared function.
non_central_chi_sq_pdf	Evaluates the noncentral chi-squared probability density function.
non_central_F_cdf	Evaluates the noncentral F cumulative distribution function.
non_central_F_inverse_cdf	Evaluates the inverse of the noncentral F cumulative distribution function.
non_central_F_pdf	Evaluates the noncentral F probability density function.
non_central_t_cdf	Evaluates the noncentral Student’s t distribution function.
non_central_t_inv_cdf	Evaluates the inverse of the noncentral Student’s t distribution function.
non_central_t_pdf	Evaluates the noncentral Student's t probability density function.
nonlinear_optimization	Fits a nonlinear regression model using Powell's algorithm.
nonlinear_regression	Fits a nonlinear regression model.
nonparam_hazard_rate	Performs nonparametric hazard rate estimation using kernel functions and quasi-likelihoods.
normal_cdf	Evaluates the standard normal (Gaussian) distribution function.
normal_inverse_cdf	Evaluates the inverse of the standard normal (Gaussian) distribution function.
normal_one_sample	Computes statistics for mean and variance inferences using a sample from a normal population.
normal_two_sample	Computes statistics for mean and variance inferences using samples from two normal populations.

Function	Purpose Statement
omp_options	Sets various OpenMP options.
output_file	Sets the output file or the error message output file.

Function	Purpose Statement
page	Sets or retrieves the page width or length.
pareto_cdf	Evaluates the Pareto cumulative probability distribution function.
pareto_pdf	Evaluates the Pareto probability density function.
partial_autocorrelation	Computes the sample partial autocorrelation function of a stationary time series.
partial_covariances	Computes partial covariances or partial correlations from the covariance or correlation matrix.
permute_matrix	Permutes the rows or columns of a matrix.
permute_vector	Rearranges the elements of a vector as specified by a permutation.
pls_regression	Performs partial least squares regression for one or more response variables and one or more predictor variables.
poisson_cdf	Evaluates the Poisson distribution function.
poisson_pdf	Evaluates the Poisson probability function.
poly_prediction	Computes predicted values, confidence intervals, and diagnostics after fitting a polynomial regression model.
poly_regression	Performs a polynomial least-squares regression.
pooled_covariances	Computes a pooled variance-covariance from the observations.
principal_components	Computes principal components.
prop_hazards_gen_lin	Analyzes time event data via the proportional hazards model.

Function	Purpose Statement
random_arma	Generates pseudorandom ARMA process numbers.
random_beta	Generates pseudorandom numbers from a beta distribution.
random_binomial	Generates pseudorandom binomial numbers.
random_cauchy	Generates pseudorandom numbers from a Cauchy distribution.
random_chi_squared	Generates pseudorandom numbers from a chi-squared distribution.
random_exponential	Generates pseudorandom numbers from a standard exponential distribution.
random_exponential_mix	Generates pseudorandom mixed numbers from a standard exponential distribution.
random_gamma	Generates pseudorandom numbers from a standard gamma distribution.
random_general_continuous	Generates pseudorandom numbers from a general continuous distribution.
random_general_discrete	Generates pseudorandom numbers from a general discrete distribution using an alias method or optionally a table lookup method.
random_generalized_gaussian	Generates pseudorandom numbers from a generalized Gaussian distribution.
random_geometric	Generates pseudorandom numbers from a geometric distribution.
random_GFSR_table_get	Retrieves the current table used in the GFSR generator.
random_GFSR_table_set	Sets the current table used in the GFSR generator.
random_hypergeometric	Generates pseudorandom numbers from a hypergeometric distribution.
random_logarithmic	Generates pseudorandom numbers from a logarithmic distribution.
random_lognormal	Generates pseudorandom numbers from a lognormal distribution.
random_MT32_init	Initializes the 32-bit Mersenne Twister generator using an array.
random_MT32_table_get	Retrieves the current table used in the 32-bit Mersenne Twister generator.
random_MT32_table_set	Sets the current table used in the 32-bit Mersenne Twister generator.
random_MT64_init	Initializes the 64-bit Mersenne Twister generator using an array.
random_MT64_table_get	Retrieves the current table used in the 64-bit Mersenne Twister generator.
random_MT64_table_set	Sets the current table used in the 64-bit Mersenne Twister generator.
random_multinomial	Generates pseudorandom numbers from a multinomial distribution.
random_mvar_from_data	Generates pseudorandom numbers from a multivariate distribution determined from a given sample.
random_mvar_gaussian_copula	Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Gaussian Copula distribution.
random_mvar_t_copula	Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Student’s t Copula distribution.
random_neg_binomial	Generates pseudorandom numbers from a negative binomial distribution.
random_normal	Generates pseudorandom numbers from a normal, N (μ, σ2), distribution.
random_normal_multivariate	Generates pseudorandom numbers from a multivariate normal distribution.
random_npp	Generates pseudorandom numbers from a nonhomogeneous Poisson process.
random_option	Selects the uniform (0, 1) multiplicative congruential pseudorandom number generator.
random_option_get	Retrieves the uniform (0, 1) multiplicative congruential pseudorandom number generator.
random_order_normal	Generates pseudorandom order statistics from a standard normal distribution.
random_order_uniform	Generates pseudorandom order statistics from a uniform (0, 1) distribution.
random_orthogonal_matrix	Generates a pseudorandom orthogonal matrix or a correlation matrix.
random_permutation	Generates a pseudorandom permutation.
random_poisson	Generates pseudorandom numbers from a Poisson distribution.
random_sample	Generates a simple pseudorandom sample from a finite population.
random_sample_indices	Generates a simple pseudorandom sample of indices.
random_seed_get	Retrieves the current value of the seed used in the IMSL random number generators.
random_seed_set	Initializes a random seed for use in the IMSL random number generators.
random_sphere	Generates pseudorandom points on a unit circle or K-dimensional sphere.
random_stable	Generates pseudorandom numbers from a stable distribution.
random_student_t	Generates pseudorandom Student's t.
random_substream_seed_get	Retrieves a seed for the congruential generators that do not do shuffling that will generate random numbers beginning 100,000 numbers farther along.
random_table_get	Retrieves the current table used in the shuffled generator.
random_table_set	Sets the current table used in the shuffled generator.
random_table_twoway	Generates a pseudorandom two-way table.
random_triangular	Generates pseudorandom numbers from a triangular distribution.
random_uniform	Generates pseudorandom numbers from a uniform (0, 1) distribution.
random_uniform_discrete	Generates pseudorandom numbers from a discrete uniform distribution.
random_von_mises	Generates pseudorandom numbers from a von Mises distribution.
random_weibull	Generates pseudorandom numbers from a Weibull distribution.
randomness_test	Performs a test for randomness.
ranks	Computes the ranks, normal scores, or exponential scores for a vector of observations.
rcbd_factorial	Analyzes data from balanced and unbalanced randomized complete-block experiments.
regression	Fits a multiple linear regression model using least squares.
regression_arima	Fits a univariate, non-seasonal ARIMA time series model with the inclusion of one or more regression variables.
regression_prediction	Computes predicted values, confidence intervals, and diagnostics after fitting a regression model.
regression_selection	Selects the best multiple linear regression models.
regression_stepwise	Builds multiple linear regression models using forward selection, backward selection or stepwise selection.
regression_summary	Produces summary statistics for a regression model given the information from the fit.
regressors_for_glm	Generates regressors for a general linear model.
robust_covariances	Computes a robust estimate of a covariance matrix and mean vector.

Function	Purpose Statement
scale_filter	Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.
seasonal_fit	Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.
set_user_fcn_return_flag	Indicates a condition has occurred in a user-supplied function necessitating a return to the calling function.
shapiro_wilk_normality_test	Performs the Shapiro-Wilk test for normality.
sign_test	Performs a sign test.
simple_statistics	Computes basic univariate statistics.
sort_data	Sorts observations by specified keys, with option to tally cases into a multi-way frequency table.
split_plot	Analyzes a wide variety of split-plot experiments with fixed, mixed or random factors.
split_split_plot	Analyzes data from split-split-plot experiments.
strip_plot	Analyzes data from strip-plot experiments.
strip_split_plot	Analyzes data from strip-split-plot experiments.
support_vector_trainer	Trains a Support Vector Machines classifier
support_vector_classification	Classifies patterns using a previously trained Support Vector Machines classifier
survival_estimates	Estimates using various parametric models.
survival_glm	Analyzes survival data using a generalized linear model.
svm_classifier_free	Frees memory allocated for a Support Vector Machines classifier

Function	Purpose Statement
t_cdf	Evaluates the Student's t distribution function.
t_inverse_cdf	Evaluates the inverse of the Student's t distribution function.
table_oneway	Tallies observations into one-way frequency table.
table_twoway	Tallies observations into a two-way frequency table.
tie_statistics	Computes tie statistics for a sample of observations.
time_series_class_filter	Converts time series data sorted with nominal classes in decreasing chronological order to useful format for processing by a neural network.
time_series_filter	Converts time series data to the format required for processing by a neural network.
ts_outlier_forecast	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.
ts_outlier_identification	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
unsupervised_nominal_filter	Converts nominal data into a series of binary encoded columns for input to a neural network.
unsupervised_ordinal_filter	Converts ordinal data into percentages.

Function	Purpose Statement
version	Returns integer information describing the version of the library, license number, operating system, and compiler.
vector_autoregression	Estimates a vector auto-regressive time series model with optional moving average components.

Function	Purpose Statement
wilcoxon_rank_sum	Performs a Wilcoxon rank sum test.
wilcoxon_sign_rank	Performs a Wilcoxon sign rank test.
write_apriori_itemsets	Prints frequent itemsets.
write_association_rules	Prints association rules.
write_matrix	Prints a rectangular matrix (or vector) stored in contiguous memory locations.
write_options	Sets or retrieves an option for printing a matrix.