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]

A

 

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.

B

 

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.

C

 

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.

D

 

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.

E

 

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).

F

 

Function

Purpose Statement

factor_analysis

Extracts initial factor-loading estimates in factor analysis.

false_discovery_rates

Calculate the False Discovery Rate (FDR) qvalues 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.

G

 

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.

H

 

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.

I

 

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.

K

 

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.

L

 

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).

M

 

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.

N

 

Function

Purpose Statement

naive_bayes_classification

Classifies unknown patterns using a previously trained NaĂ¯ve Bayes classifier.

naive_bayes_trainer

Trains a NaĂ¯ve 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.

O

 

Function

Purpose Statement

omp_options

Sets various OpenMP options.

output_file

Sets the output file or the error message output file.

P

 

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.

R

 

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 (μσ2), 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.

S

 

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

T

 

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.

U

 

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.

V

 

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.

W

 

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.