C............ ELEMENTARY AND SPECIAL FUNCTIONS (search also class L5)
C3.......... Polynomials
C3a........ Orthogonal
OPOLY
Generate orthogonal polynomials with respect to x values and specified weights.
C7.......... Gamma
C7e........ Incomplete gamma
CHIDF
Evaluate the chi-squared distribution function.
CHIIN
Evaluate the inverse of the chi-squared distribution function.
GAMDF
Evaluate the gamma distribution function.
GAMIN
Evaluate the inverse of the gamma distribution function.
C7f......... Incomplete gamma
BETDF
Evaluate the beta probability distribution function.
BETIN
Evaluate the inverse of the beta distribution function.
C8.......... Error functions
C8a........ Error functions, their inverses, integrals, including the normal distribution function
ANORDF
Evaluate the standard normal (Gaussian) distribution function.
ANORIN
Evaluate the inverse of the standard normal (Gaussian) distribution function.
K............ APPROXIMATION (search also class L8)
K1.......... Least squares (L2) approximation
K1a........ Linear least squares (search also classes D5, D6, D9)
K1a1...... Unconstrained
RCOV
Fit a multiple linear regression model given the variance-covariance matrix.
RGIVN
Fit a multivariate linear regression model via fast Givens transformations.
RGLM
Fit a multivariate general linear model.
RLSE
Fit a multiple linear regression model using least squares.
K1a1a.... Univariate data (curve fitting)
K1ala2... Polynomials
RCURV
Fit a polynomial curve using least squares.
RFORP
Fit an orthogonal polynomial regression model.
RPOLY
Analyze a polynomial regression model.
K1a2...... Constrained
K1a2a.... Linear constraints
RLEQU
Fit a multivariate linear regression model with linear equality restrictions HΒ = G imposed on the regression parameters given results from IMSL routine RGIVN after IDO = 1 and IDO = 2 and prior to IDO = 3.
K1b........ Nonlinear least squares
K1b1...... Unconstrained
K1b1a.... Smooth functions
K1b1a1.. User provides no derivatives
RNLIN
Fit a nonlinear regression model.
K1b1a2.. User provides first derivatives
RNLIN
Fit a nonlinear regression model.
K2.......... Minimax (L∞) approximation
RLMV
Fit a multiple linear regression model using the minimax criterion.
K3.......... Least absolute value (L1) approximation
RLLP
Fit a multiple linear regression model using the Lp norm criterion.
K4.......... Other analytic approximations (e.g., Taylor polynomial, Pade)
RLLP
Fit a multiple linear regression model using the Lp norm criterion.
L............. STATISTICS, PROBABILITY
L1........... Data summarization
L1a......... One-dimensional data
L1a1....... Raw data
EQTIL
Compute empirical quantiles.
LETTR
Produce a letter value summary.
ORDST
Determine order statistics.
L1a1a..... Location
UVSTA
Compute basic univariate statistics.
L1a1b.... Disperson
UVSTA
Compute basic univariate statistics.
L1a1c..... Shape
UVSTA
Compute basic univariate statistics.
L1a1e..... Ties
NTIES
Compute tie statistics for a sample of observations.
L1a3....... Grouped data
GRPES
Compute basic statistics from grouped data.
L1c......... Multi-dimensional data
L1c1....... Raw data
CSTAT
Compute cell frequencies, cell means, and cell sums of squares for multivariate data.
L1c1b.... Covariance, correlation
CORVC
Compute the variance-covariance or correlation matrix.
PCORR
Compute partial correlations or covariances from the covariance or correlation matrix.
RBCOV
Compute a robust estimate of a covariance matrix and mean vector.
L2........... Data manipulation
L2a......... Transform (search also classes L10a, N6, and N8)
BCTR
Perform a forward or an inverse Box-Cox (power) transformation.
GCSCP
Generate centered variables, squares, and crossproducts.
OPOLY
Generate orthogonal polynomials with respect to x values and specified weights.
RANKS
Compute the ranks, normal scores, or exponential scores for a vector of observations.
L2b........ Tally
CSTAT
Compute cell frequencies, cell means, and cell sums of squares for multivariate data.
FREQ
Tally multivariate observations into a multi-way frequency table.
OWFRQ
Tally observations into a one-way frequency table.
TWFRQ
Tally observations into a two-way frequency table.
L2e......... Construct new variables (e.g., indicator variables)
GRGLM
Generate regressors for a general linear model.
L3........... Elementary statistical graphics (search also class Q)
L3a......... One-dimensional data
L3a1....... Histograms
HHSTP
Print a horizontal histogram.
VHSTP
Print a vertical histogram.
L3a2....... Frequency, cumulative frequency, percentile plots
CDFP
Print a sample cumulative distribution function (CDF), a theoretical CDF, and confidence band information.
L3a3....... EDA graphics (e.g., box plots)
BOXP
Print boxplots for one or more samples.
STMLP
Print a stem-and-leaf plot.
L3a4....... Bar charts
HHSTP
Print a horizontal histogram.
VHSTP
Print a vertical histogram.
L3b........ Two-dimensional data (search also class L3e)
L3b1...... Histograms (superimposed and bivariate)
VHS2P
Print a vertical histogram with every bar subdivided into two parts.
L3b2...... Frequency, cumulative frequency
CDF2P
Print a plot of two sample cumulative distribution functions.
L3e......... Multi-dimensional data
L3e3....... Scatter diagrams
L3e3a..... Superimposed Y vs. X
PLOTP
Print a plot of up to ten sets of points.
SCTP
Print a scatterplot of several groups of data.
L3e4....... EDA
BOXP
Print boxplots for one or more samples.
L4........... Elementary data analaysis
L4a......... One-dimensional data
L4a1....... Raw data
L4a1a..... Parametric analysis
CDFP
Print a sample cumulative distribution function (CDF), a theoretical CDF, and confidence band information.
L4a1a2... Probability plots
L4a1a2e. Exponential, extreme value
PROBP
Print a probability plot.
L4a1a2h Halfnormal
PROBP
Print a probability plot.
L4a1a21. Lambfa, logistic, lognormal
PROBP
Print a probability plot.
L4a1a2n Negative binomial, normal
PROBP
Print a probability plot.
L4a1a2w Weibull
PROBP
Print a probability plot.
L4a1a4... Parameter estimates and tests
MLE Calculates maximum likelihood estimates for the parameters of one of several univariate probability distributions.
L4a1a4b Binomial
BINES
Estimate the parameter p of the binomial distribution.
L4a1a4p Poisson
POIES
Estimate the parameter of the Poisson distribution.
L4a1b.... Nonparametric analysis
L4a1b1.. Estimates and test regarding location (e.g., median), dispersion and shape
SIGNT
Perform a sign test of the hypothesis that a given value is a specified quantile of a distribution.
SNRNK
Perform a Wilcoxon signed rank test.
L4a1b2.. Density function estimation
DESKN
Perform nonparametric probability density function estimation by the kernel method.
DESPL
Perform nonparametric probability density function estimation by the penalized likelihood method.
DESPT
Estimate a probability density function at specified points using linear or cubic interpolation.
DNFFT
Compute Gaussian kernel estimates of a univariate density via the fast Fourier transform over a fixed interval.
L4a1c..... Goodness-of-fit tests
ADNRM Performs an Anderson-Darling test for normality.
CHIGF
Perform a chi-squared goodness-of-fit test.
CVMNRM Performs a Cramer-von Mises test for normality.
KSONE
Perform a Kolmogorov-Smirnov one-sample test for continuous distributions.
LILLF
Perform Lilliefors test for an exponential or normal distribution.
SPWLK
Perform a Shapiro-Wilk W-test for normality.
L4ald..... Analysis of a sequnce of numbers (search also class L10a)
DCUBE
Perform a triplets test.
DSQAR
Perform a D-square test.
NCTRD
Perform the Noether test for cyclical trend.
PAIRS
Perform a pairs test.
RUNS
Perform a runs up test.
SDPLC
Perform the Cox and Stuart sign test for trends in dispersion and location.
L4a3....... Grouped (and/or censored) data
GRPES
Compute basic statistics from grouped data.
NRCES
Compute maximum likelihood estimates of the mean and variance from grouped and/or censored normal data.
L4a4....... Data sampled from a finite population
SMPPR Compute statistics for inferences regarding the population proportion and total, given proportion data from a simple random sample.
SMPPS
Compute statistics for inferences regarding the population proportion and total, given proportion data from a stratified random sample.
SMPSC
Compute statistics for inferences regarding the population mean and total using single-stage cluster sampling with continuous data.
SMPSR
Compute statistics for inferences regarding the population mean and total, given data from a simple random sample.
SMPSS
Compute statistics for inferences regarding the population mean and total, given data from a stratified random sample.
SMPST
Compute statistics for inferences regarding the population mean and total, given continuous data from a two-stage sample with equisized primary units.
L4b........ Two dimensional data (search also class L4c)
L4b1...... Pairwise independent data
L4b1a.... Parametric analysis
L4b1a4.. Parameter estimates and hypothesis tests
TWOMV
Compute statistics for mean and variance inferences using samples from two normal populations.
L4b1b.... Nonparametric analysis (e.g., tests based on ranks)
CNCRD
Calculate and test the significance of the Kendall coefficient of concordance.
INCLD
Perform an includance test.
KENDL
Compute and test Kendall’s rank correlation coefficient.
RNKSM
Perform the Wilcoxon rank sum test.
L4b1c.... Goodness-of-fit tests
KSTWO
Perform a Kolmogorov-Smirnov two-sample test.
L4b4...... Pairwise dependent grouped data
CTRHO
Estimate the bivariate normal correlation coefficient using a contingency table.
TETCC
Categorize bivariate data and compute the tetrachoric correlation coefficient.
L4b5...... Data sampled from a finite population
SMPRR
Compute statistics for inferences regarding the population mean and total using ratio or regression estimation, or inferences regarding the population ratio, given a simple random sample.
SMPRS
Compute statistics for inferences regarding the population mean and total using ratio or regression estimation, given continuous data from a stratified random sample.
L4c......... Multi-dimensional data (search also classes L4b and L7a1)
L4c1....... Independent data
L4c1b.... Nonparametric analysis
BHAKV
Perform a Bhapkar V test.
KRSKL
Perform a Kruskal-Wallis test for identical population medians.
KTRND
Perform a k-sample trends test against ordered alternatives.
MVMMT
Compute Mardia’s multivariate measures of skewness and kurtosis and test for multivariate normality.
QTEST
Perform a Cochran Q test for related observations.
L4e......... Multiple multi-dimensional data sets
MVIND
Compute a test for the independence of k sets of multivariate normal variables.
L5........... Function evaluation (search also class C)
L5a......... Univariate
L5a1....... Cumulative distribution functions, probability density functions
L5a1b.... Beta, binomial
BETDF Evaluate the beta probability distribution function.
BETNDF Evaluate the noncentral beta cumulative distribution function.
BETNPR Evaluate the noncentral beta probability density function.
BINDF Evaluate the binomial distribution function.
BINPR Evaluate the binomial probability function.
L5a1c..... Cauchy, chi-squared
CHIDF
Evaluate the chi-squared distribution function.
CSNDF
Evaluate the noncentral chi-squared distribution function.
CSNPR
Evaluates the noncentral chi-squared probability density function.
L5a1f..... F distribution
FDF Evaluate the F distribution function.
FNDF Evaluate the noncentral F cumulative distribution function (CDF).
FNPR Evaluate the noncentral F probability density function.
L5a1g.... Gamma, general, geometric
GAMDF
Evaluate the gamma distribution function.
GCDF
Evaluate a general continuous cumulative distribution function given ordinates of the density.
L5a1h.... Halfnormal, hyergeometric
HYPDF
Evaluate the hypergeometric distribution function.
HYPPR
Evaluate the hypergeometric probability function.
L5a1k..... Kendall F statistic, Kolmogorsv-Smirnov
AKS1DF
Evaluate the distribution function of the one-sided Kolmogorov-Smirnov goodness-of-fit D+ or D− test statistic based on continuous data for one sample.
AKS2DF
Evaluate the distribution function of the Kolmogorov-Smirnov goodness-of-fit D test statistic based on continuous data for two samples.
KENDP
Compute the frequency distribution of the total score in Kendall’s rank correlation coefficient.
L5a1n.... Negative binomial, normal
ANORDF
Evaluate the standard normal (Gaussian) distribution function.
L5a1p.... Pareto, Poisson
POIDF
Evaluate the Poisson distribution function.
POIPR
Evaluate the Poisson probability function.
L5a1t..... t distribution
TDF
Evaluate the Student’s t distribution function.
TNDF
Evaluate the noncentral Student’s t distribution function.
TNPR
Evaluate the noncentral Student's t probability density function.
L5a2....... Inverse cumulative distribution functions, sparsity functions
L5a2b.... Beta, binomial
BETIN
Evaluate the inverse of the beta distribution function.
BETNIN This function evaluates the inverse of the noncentral beta cumulative distribution function (CDF).
BETNPR This function evaluates the noncentral beta probability density function.
L5a2c..... Cauchy, chi-squared
CHIIN
Evaluate the inverse of the chi-squared distribution function.
CSNIN
Evaluate the inverse of the noncentral chi-squared function.
L5a2f..... F distribution
FIN
Evaluate the inverse of the F distribution function.
FNIN Evaluate the inverse of the F cumulative distribution function (CDF).
L5a2g.... Gamma, general, geometric
GAMIN
Evaluate the inverse of the gamma distribution function.
GCIN
Evaluate the inverse of a general continuous cumulative distribution function given ordinates of the density.
GFNIN
Evaluate the inverse of a general continuous cumulative distribution function given in a subprogram.
L5a2t..... t distribution
TIN
Evaluate the inverse of the Student’s t distribution function.
TNIN
Evaluate the inverse of the noncentral Student’s t distribution function.
L5b........ Multivariate
L5b1...... Cumulative distribution functions, probability density functions
L5b1n.... Normal
BNRDF
Evaluate the bivariate normal distribution function.
L6........... Random number generation
L6a......... Univariate
L6a2....... Beta, binomial, Boolean
RNBET
Generate pseudorandom numbers from a beta distribution.
RNBIN
Generate pseudorandom numbers from a binomial distribution.
L6a3....... Cauchy, chi-squared
RNCHI
Generate pseudorandom numbers from a chi-squared distribution.
RNCHY
Generate pseudorandom numbers from a Cauchy distribution.
L6a5....... Exponential, extreme value
RNEXP
Generate pseudorandom numbers from a standard exponential distribution.
RNEXT
Generate pseudorandom numbers from a mixture of two exponential distributions.
L6a7....... Gamma, general (continuous, discrete), geometric
RNGAM
Generate pseudorandom numbers from a standard gamma distribution.
RNGCS
Set up table to generate pseudorandom numbers from a general continuous distribution.
RNGCT
Generate pseudorandom numbers from a general continuous distribution.
RNGDA
Generate pseudorandom numbers from a general discrete distribution using an alias method.
RNGDS
Set up table to generate pseudorandom numbers from a general discrete distribution.
RNGDT
Generate pseudorandom numbers from a general discrete distribution using a table lookup method.
RNGEO
Generate pseudorandom numbers from a geometric distribution.
L6a8....... Halfnormal, hypergeometric
RNHYP
Generate pseudorandom numbers from a hypergeometric distribution.
L6a12..... Lambda, logistic, lognormal
RNLGR
Generate pseudorandom numbers from a logarithmic distribution.
RNLNL
Generate pseudorandom numbers from a lognormal distribution.
L6a14..... Negative binomial, normal, normal order statistics
RNNBN
Generate pseudorandom numbers from a negative binomial distribution.
RNNOA
Generate pseudorandom numbers from a standard normal distribution using an acceptance/rejection method.
RNNOF
Generate a pseudorandom number from a standard normal distribution.
RNNOR
Generate pseudorandom numbers from a standard normal distribution using an inverse CDF method.
RNNOS
Generate pseudorandom order statistics from a standard normal distribution.
L6a16..... Pareto, Pascal, permutations, Poisson
RNNPP
Generate pseudorandom numbers from a nonhomogeneous Poisson process.
RNPER
Generate a pseudorandom permutation.
RNPOI
Generate pseudorandom numbers from a Poisson distribution.
L6a19..... Samples, stable distribution
RNSRI
Generate a simple pseudorandom sample of indices.
RNSRS
Generate a simple pseudorandom sample from a finite population.
RNSTA
Generate pseudorandom numbers from a stable distribution.
L6a20..... t distribution, time series, triangular
RNARM
Generate a time series from a specified ARMA model.
RNNPP
Generate pseudorandom numbers from a nonhomogeneous Poisson process.
RNSTT
Generate pseudorandom numbers from a Student’s t distribution.
RNTRI
Generate pseudorandom numbers from a triangular distribution on the interval (0,1).
L6a21..... Uniform (continuous, discrete), uniform order statistics
RNUN
Generate pseudorandom numbers from a uniform (0,1) distribution.
RNUND
Generate pseudorandom numbers from a discrete uniform distribution.
RNUNF
Generate a pseudorandom number from a uniform (0, 1) distribution.
RNUNO
Generate pseudorandom order statistics from a uniform (0, 1) distribution.
L6a22..... Von Mises
RNVMS
Generate pseudorandom numbers from a von Mises distribution.
L6a23..... Weibull
RNWIB
Generate pseudorandom numbers from a Weibull distribution.
L6b........ Multivariate
RNDAT
Generate pseudorandom numbers from a multivariate distribution determined from a given sample.
RNMVGC
Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Gaussian Copula distribution.
RNMVTC
Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Student‘s t Copula distribution.
L6b3...... Contingency table, correlation matrix
RNCOR
Generate a pseudorandom orthogonal matrix or a correlation matrix.
RNTAB
Generate a pseudorandom two-way table.
L6b13.... Multinomial
RNMTN
Generate pseudorandom numbers from a multinomial distribution.
L6b14.... Normal
RNMVN
Generate pseudorandom numbers from a multivariate normal distribution.
L6b15.... Orthogonal matrix
RNCOR
Generate a pseudorandom orthogonal matrix or a correlation matrix.
L6b21.... Linear L-1 (least absolute value) approximation random numbers
FAURE_INIT Shuffles Faure sequence initialization.
FAURE_FREE Frees the structure containing information about the Faure sequence.
FAURE_NEXT Computes a shuffled Faure sequence.
L6b21.... Uniform
RNSPH
Generate pseudorandom points on a unit circle or K-dimensional sphere.
L6c......... Service routines (e.g., seed)
RNGEF Retrieve the current value of the array used in the IMSL GFSR random number generator.
RNGES Retrieve the current value of the table in the IMSL random number generators that use shuffling.
RNGET Retrieve the current value of the seed used in the IMSL random number generators.
RNISD Determine a seed that yields a stream beginning 100,000 numbers beyond the beginning of the stream yielded by a given seed used in IMSL multiplicative congruential generators (with no shufflings).
RNOPG Retrieve the indicator of the type of uniform random number generator.
RNOPT Select the uniform (0, 1) multiplicative congruential pseudorandom number generator.
RNSEF Initialize the array used in the IMSL GFSR random number generator.
RNSES Initialize the table in the IMSL random number generators that use shuffling.
RNSET Initialize a random seed for use in the IMSL randomnumber generators.
L7........... Analysis of variance (including analysis of covariance)
L7a......... One-way
L7a1....... Parametric
AONEC
Analyze a one-way classification model with covariates.
AONEW
Analyze a one-way classification model.
CTRST
Compute contrast estimates and sums of squares.
SCIPM
Compute simultaneous confidence intervals on all pairwise differences of means.
SNKMC
Perform Student-Newman-Keuls multiple comparison test.
L7b........ Two-way (search also class L7d)
ATWOB
Analyze a randomized block design or a two-way balanced design.
FRDMN
Perform Friedman’s test for a randomized complete block design.
MEDPL
Compute a median polish of a two-way table.
L7c......... Three-way (e.g., Latin squares) (search also class L7d)
ALATN
Analyze a Latin square design.
L7d........ Multi-way
L7d1...... Balanced complete data (e.g., factorial designs)
ABALD
Analyze a balanced complete experimental design for a fixed, random, or mixed model.
ANEST
Analyze a completely nested random model with possibly unequal numbers in the subgroups.
ANWAY
Analyze a balanced n-way classification model with fixed effects.
CIDMS
Compute a confidence interval on a variance component estimated as proportional to the difference in two mean squares in a balanced complete experimental design.
ROREX
Reorder the responses from a balanced complete experimental design.
L7d2...... Balanced incomplete data (e.g., fractional factorial designs)
ABIBD
Analyze a balanced incomplete block design or a balanced lattice design.
L7d3...... General linear models (unbalanced data)
ANEST
Analyze a completely nested random model with possibly unequal numbers in the subgroups.
RGLM
Fit a multivariate general linear model.
L7e......... Multivariate
RGLM
Fit a multivariate general linear model.
L7f......... Generate experimental designs
RCOMP
Generate an orthogonal central composite design.
L8........... Regression (search also classes D5, D6, D9, G, K)
L8a......... Simple linear (e.g., y = β0 + β1x + ɛ)
L8a1....... Ordinary least squares
RONE
Analyze a simple linear regression model.
L8a1a..... Parameter estimation
L8a1a1... Unweighted data
RLINE
Fit a line to a set of data points using least squares.
L8a1d.... Inference (e.g., calibration) (search also class L8a1a)
RINCF
Perform response control given a fitted simple linear regression model.
RINPF
Perform inverse prediction given a fitted simple linear regression model.
L8a2....... Lp for p different from 2 (e.g., least absolute value, minimax)
RLAV
Fit a multiple linear regression model using the least absolute values criterion.
RLLP
Fit a multiple linear regression model using the Lp norm criterion.
RLMV
Fit a multiple linear regression model using the minimax criterion.
L8b........ Polynomial (e.g., y = β0 + β1x + β2x2 + ɛ) (search also class L8c)
L8b1...... Ordinary least squares
L8b1a.... Degree determination
RFORP
Fit an orthogonal polynomial regression model.
RPOLY
Analyze a polynomial regression model.
L8b1b.... Parameter estimation
L8b1b2.. Using orthogonal polynomials
RCURV
Fit a polynomial curve using least squares.
RFORP
Fit an orthogonal polynomial regression model.
RPOLY
Analyze a polynomial regression model.
L8b1c.... Analysis (search also class L8b1b)
RCASP
Compute case statistics for a polynomial regression model given the fit based on orthogonal polynomials.
RPOLY
Analyze a polynomial regression model.
RSTAP
Compute summary statistics for a polynomial regression model given the fit based on orthogonal polynomials.
L8b1d.... Inference (search also class L8b1b)
RCASP
Compute case statistics for a polynomial regression model given the fit based on orthogonal polynomials.
RPOLY
Analyze a polynomial regression model.
RSTAP
Compute summary statistics for a polynomial regression model given the fit based on orthogonal polynomials.
L8c......... Multiple linear (e.g., y = β0 + β1x1 +…+ βkxk + ɛ)
PLSR
Performs partial least squares regression for one or more response variables and a set of one or more predictor variables.
L8c1....... Ordinary least squares
L8c1a..... Variable selection
L8c1a2... Using correlation or covariance data
GSWEP
Perform a generalized sweep of a row of a nonnegative definite matrix.
RBEST
Select the best multiple linear regression models.
RSTEP
Build multiple linear regression models using forward selection, backward selection, or stepwise selection.
L8c1b.... Parameter estimation (search also class L8c1a)
L8c1b1.. Using raw data
RGIVN
Fit a multivariate linear regression model via fast Givens transformations.
RGLM
Fit a multivariate general linear model.
RLSE
Fit a multiple linear regression model using least squares.
L8c1b2.. Using correlation data
RCOV
Fit a multiple linear regression model given the variance-covariance matrix.
L8c1c..... Analysis (search also classes L8c1a and L8c1b)
RCASE
Compute case
statistics and diagnostics given data points, coefficient estimates, and the R matrix for a fitted
general linear model.
RCOVB Compute the estimated variance-covariance matrix of the estimated regression coefficients given the R matrix.
RLOFE Compute a lack-of-fit test based on exact replicates for a fitted regression model.
RLOFN Compute a lack-of-fit test based on near replicates for a fitted regression model.
ROTIN Compute diagnostics for detection of outliers and influential data points given residuals and the R matrix for a fitted general linear model.
RSTAT
Compute
statistics related to a regression fit given the coefficient estimates and the R matrix.
L8c1d.... Inference (search also classes L8c1a and L8c1b)
CESTI Construct an equivalent completely testable multivariate general linear hypothesis HBU = G from a partially testable hypothesis HpBU = Gp.
RCASE
Compute case
statistics and diagnostics given data points, coefficient estimates , and the R matrix for a fitted
general linear model.
RHPSS
Compute the
matrix of sums of squares and crossproducts for the multivariate general linear
hypothesis HBU = G given the coefficient estimates and the R matrix.
RHPTE
Perform tests for a multivariate general linear hypothesis HBU = G given the hypothesis sums of squares and crossproducts matrix SH and the error sums of squares and crossproducts matrix SE.
RSTAT
Compute
statistics related to a regression fit given the coefficient estimates and the R matrix.
L8c3....... Lp for p different from 2
RLAV
Fit a multiple linear regression model using the least absolute values criterion.
RLLP
Fit a multiple linear regression model using the Lp norm criterion.
RLMV
Fit a multiple linear regression model using the minimax criterion.
L8d........ Polynomial in several variables
RCOMP
Generate an orthogonal central composite design.
TCSCP
Transform coefficients from a quadratic regression model generated from squares and crossproducts of centered variables to a model using uncentered variables.
L8e......... Nonlinear (i.e., y = f(X; θ) + ɛ)
L8e1....... Ordinary least squares
L8e1b.... Parameter estimation
RNLIN
Fit a nonlinear regression model.
L8f......... Simultaneous (i.e., Y = XB + ɛ)
RCOV
Fit a multiple linear regression model given the variance-covariance matrix.
RGIVN
Fit a multivariate linear regression model via fast Givens transformations.
RGLM
Fit a multivariate general linear model.
RHPSS
Compute the
matrix of sums of squares and crossproducts for the multivariate general linear
hypothesis HBU = G given the coefficient estimates and the R matrix.
RHPTE
Perform tests for a multivariate general linear hypothesis HBU = G given the hypothesis sums of squares and crossproducts matrix SH and the error sums of squares and crossproducts matrix SE.
RLEQU
Fit a multivariate linear regression model with linear equality restrictions HΒ = G imposed on the regression parameters given results from IMSL routine RGIVN after IDO = 1 and IDO = 2 and prior to IDO = 3.
L8i.......... Service routines (e.g., matrix manipulation for variable selection)
GCLAS
Get the unique values of each classification variable.
GCSCP
Generate centered variables, squares, and crossproducts.
GRGLM
Generate regressors for a general linear model.
RORDM
Reorder rows and columns of a symmetric matrix.
RSUBM
Retrieve a symmetric submatrix from a symmetric matrix.
L9........... Categorical data analysis
CTGLM
Analyze categorical data using logistic, Probit, Poisson, and other generalized linear models.
CTRAN
Perform generalized Mantel-Haenszel tests in a stratified contingency table.
L9a......... 2-by-2 tables
CTTWO
Perform a chi-squared analysis of a 2 by 2 contingency table.
L9b........ Two-way tables (search also class L9d)
CTCHI
Perform a chi-squared analysis of a two-way contingency table.
CTEPR
Compute Fisher’s exact test probability and a hybrid approximation to the Fisher exact test probability for a contingency table using the network algorithm.
CTPRB
Compute exact probabilities in a two-way contingency table.
CTRHO
Estimate the bivariate normal correlation coefficient using a contingency table.
CTWLS
Perform a generalized linear least squares analysis of transformed probabilities in a two-dimensional contingency table.
MEDPL
Compute a median polish of a two-way table.
TWFRQ
Tally observations into a two-way frequency table.
L9c......... Log-linear model
CTASC
Compute partial association statistics for log-linear models in a multidimensional contingency table.
CTLLN
Compute model estimates and associated statistics for a hierarchical log-linear model.
CTPAR
Compute model estimates and covariances in a fitted log-linear model.
CTSTP
Build hierarchical log-linear models using forward selection, backward selection, or stepwise selection.
PRPFT
Perform iterative proportional fitting of a contingency table using a loglinear model.
L9d........ EDA (e.g., median polish)
MEDPL
Compute a median polish of a two-way table.
L10......... Time series analysis (search also class J)
L10a....... Univariate
REG_ARIMA
............. Fits a univariate, non seasonal ARIMA time series model with the inclusion of one or more regression variables.
L10a1..... Transformations
L10a1b.. Stationarity (search also class L8a1)
BCTR
Perform a forward or an inverse Box-Cox (power) transformation.
L10a1c... Filters
L10a1c1. Difference (nonseasonal and seasonal)
DIFF
Difference a time series.
L10a2..... Time domain analysis
AUTO_ARIMA Automatically identifies time series outliers, determines parameters of a multiplicative seasonal ARIMA ( p,0, q)×(0, d,0)s model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.
AUTO_FPE_MUL_AR Automatic selection and fitting of a multivariate autoregressive time series model using Akaike’s Multivariate Final Prediction Error (MFPE) criteria.
AUTO_FPE_UNI_AR Automatic selection and fitting of a univariate autoregressive time series model using Akaike’s Final Prediction Error (FPE) criteria.
AUTO_MUL_AR Automatic selection and fitting of a multivariate autoregressive time series model.
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.
BAY_SEA Model allows for a decomposition of a time series into trend, seasonal, and an error component.
GARCH Computes estimates of the parameters of a GARCH (p,q) model.
MAX_ARMA Exact maximum likelihood estimation of the parameters in a univariate ARMA (auto-regressive, moving average) time series model.
TS_OUTLIER_FORECAST Detects and determines outliers and simultaneously estimates the model parameters in a time series.
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.
L10a2a... Summary statistics
L10a2a1. Autocovariances and autocorrelations
ACF
Compute the sample autocorrelation function of a stationary time series.
LOFCF
Perform lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function.
L10a2a2. Partial autocorrelations
PACF
Compute the sample partial autocorrelation function of a stationary time series.
L10a2c... Autoregressive models
SPWF
Compute the Wiener forecast operator for a stationary stochastic process.
L10a2d.. ARMA and ARIMA models (including Box-Jenkins methods)
AUTO_PARM
Estimates structural breaks in non-stationary univariate time series.
REG_ARIMA
Fits a univariate, non seasonal ARIMA time series model with the inclusion of one or more regression variables.
L10a2d2 Parameter estimation
ARMME
Compute method of moments estimates of the autoregressive parameters of an ARMA model.
MAMME
Compute method of moments estimates of the moving average parameters of an ARMA model.
NSLSE
Compute least squares estimates of parameters for a nonseasonal ARMA model.
NSPE
Compute preliminary estimates of the autoregressive and moving average parameters of an ARMA model.
MAX_ARMA Exact maximum likelihood estimation of the parameters in a univariate ARMA (auto-regressive, moving average) time series model.
L10a2d3 Forecasting
NSBJF
Compute Box-Jenkins forecasts and their associated probability limits for a nonseasonal ARMA model.
L10a2e... State-space analysis (e.g., Kalman filtering)
KALMN
Perform Kalman filtering and evaluate the likelihood function for the state-space model.
L10a3..... Frequency domain analysis (search also class J1)
L10a3a... Spectral Analysis
ARMA_SPEC Calculates the rational power spectrum for an ARMA model.
L10a3a2. Periodogram analysis
PFFT
Compute the periodogram of a stationary time series using a fast Fourier transform.
L10a3a3. Spectrum estimation using the periodogram
SSWD
Estimate the nonnormalized spectral density of a stationary time series using a spectral window given the time series data.
SSWP
Estimate the nonnormalized spectral density of a stationary time series using a spectral window given the periodogram.
SWED
Estimation of the nonnormalized spectral density of a stationary time series based on specified periodogram weights given the time series data.
SWEP
Estimation of the nonnormalized spectral density of a stationary time series based on specified periodogram weights given the periodogram.
L10a3a6. Spectral windows
DIRIC
Compute the Dirichlet kernel.
FEJER
Compute the Fejér kernel.
L10b...... Two time series (search also classes L10c and L10d)
L10b2.... Time domain analysis
L10b2a.. Summary statistics (e.g., cross-correlations)
CCF
Compute the sample cross-correlation function of two stationary time series.
L10b2b.. Transfer function models
IRNSE
Compute estimates of the impulse response weights and noise series of a univariate transfer function model.
TFPE
Compute preliminary estimates of parameters for a univariate transfer function model.
L10b3.... Frequency domain analysis (search also class J1)
L10b3a.. Cross-spectral analysis
L10b3a3 Cross-spectrum estimation using the cross-periodogram
CSSWD
Estimate the nonnormalized cross-spectral density of two stationary time series using a spectral window given the time series data.
CSSWP
Estimate the nonnormalized cross-spectral density of two stationary time series using a spectral window given the spectral densities and cross periodogram.
CSWED
Estimate the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the time series data.
CSWEP
Estimate the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the spectral densities and cross periodogram.
L10c....... Multivariate time series (search also classes J1, L3e3 and L10b)
KALMN
Perform Kalman filtering and evaluate the likelihood function for the state-space model.
OPT_DES Allows for multiple channels for both the controlled and manipulated variables
L10d...... Two multi-channel time series
MCCF
Compute the multichannel cross-correlation function of two mutually stationary multichannel time series.
MLSE
Compute least squares estimates of a linear regression model for a multichannel time series with a specified base channel.
MWFE
Compute least squares estimates of the multichannel Wiener filter coefficients for two mutually stationary multichannel time series.
L11......... Correlation analysis (search also classes L4 and L13c)
BSCAT
Compute the biserial correlation coefficient for a dichotomous variable and a classification variable.
BSPBS
Compute the biserial and point-biserial correlation coefficients for a dichotomous variable and a numerically measurable classification variable.
CANCOR Given an input array of deviate values, generates a canonical correlation array.
CORVC
Compute the variance-covariance or correlation matrix.
COVPL
Compute a pooled variance-covariance matrix from the observations.
CTRHO
Estimate the bivariate normal correlation coefficient using a contingency table.
KENDP
Compute the frequency distribution of the total score in Kendall’s rank correlation coefficient.
PCORR
Compute partial correlations or covariances from the covariance or correlation matrix.
RBCOV
Compute a robust estimate of a covariance matrix and mean vector.
TETCC
Categorize bivariate data and compute the tetrachoric correlation coefficient.
L12......... Discriminant analysis
DMSCR
Use Fisher’s linear discriminant analysis method to reduce the number of variables.
DSCRM
Perform a linear or a quadratic discriminant function analysis among several known groups.
NNBRD
Perform a k nearest neighbor discrimination.
L13......... Covariance structures models
L13a....... Factor analysis
FACTR
Extract initial factor-loading estimates in factor analysis.
FCOEF
Compute a matrix of factor score coefficients for input to the following IMSL routine (FSCOR).
FDOBL
Compute a direct oblimin rotation of a factor-loading matrix.
FGCRF
Compute direct oblique rotation according to a generalized fourth-degree polynomial criterion.
FHARR
Compute an oblique rotation of an unrotated factor-loading matrix using the Harris-Kaiser method.
FIMAG
Compute the image transformation matrix.
FOPCS
Compute an orthogonal Procrustes rotation of a factor-loading matrix using a target matrix.
FPRMX
Compute an oblique Promax or Procrustes rotation of a factor-loading matrix using a target matrix, including pivot and power vector options.
FRESI
Compute commonalities and the standardized factor residual correlation matrix.
FROTA
Compute an orthogonal rotation of a factor-loading matrix using a generalized orthomax criterion, including quartimax, varimax, and equamax rotations.
FRVAR
Compute the factor structures and the variance explained by each factor.
FSCOR
Compute a set of factor scores given the factor score coefficient matrix.
L13b...... Principal components analysis
KPRIN
Maximum likelihood or least-squares estimates for principle components from one or more matrices.
PRINC
Compute principal components from a variance-covariance matrix or a correlation matrix.
L13c....... Canonical correlation
CANCR
Perform canonical correlation analysis from a data matrix.
CANVC
Perform canonical correlation analysis from a variance-covariance matrix or a correlation matrix.
L14......... Cluster analysis
L14a....... One-way
L14a1..... Unconstrained
L14a1a... Nested
L14a1a1. Joining (e.g., single link)
CLINK
Perform a hierarchical cluster analysis given a distance matrix.
L14a1b.. Non-nested (e.g., K means)
KMEAN Perform a K-means (centroid) cluster analysis.
L14c....... Display
TREEP
Print a binary tree.
L14d...... Service routines (e.g., compute distance matrix)
CDIST
Compute a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.
CNUMB
Compute cluster membership for a hierarchical cluster tree.
L15......... Life testing, survival analysis
ACTBL
Produce population and cohort life tables.
HAZEZ
Perform nonparametric hazard rate estimation using kernel functions. Easy-to-use version of the previous IMSL subroutine (HAZRD).
HAZRD
Perform nonparametric hazard rate estimation using kernel functions and quasi-likelihoods.
HAZST
Perform hazard rate estimation over a grid of points using a kernel function.
KAPMR
Compute Kaplan-Meier estimates of survival probabilities in stratified samples.
KTBLE
Print Kaplan-Meier estimates of survival probabilities in stratified samples.
NRCES
Compute maximum likelihood estimates of the mean and variance from grouped and/or censored normal data.
PHGLM
Analyze time event data via the proportional hazards model.
STBLE
Estimate survival probabilities and hazard rates for various parametric models.
SVGLM
Analyze censored survival data using a generalized linear model.
TRNBL
Compute Turnbull’s generalized Kaplan-Meier estimates of survival probabilities in samples with interval censoring.
L16......... Multidimensional scaling
MSDBL
Obtain normalized product-moment (double centered) matrices from dissimilarity matrices.
MSDST
Compute distances in a multidimensional scaling model.
MSIDV
Perform individual-differences multidimensional scaling for metric data using alternating least squares.
MSINI
Compute initial estimates in multidimensional scaling models.
MSSTN
Transform dissimilarity/similarity matrices and replace missing values by estimates to obtain standardized dissimilarity matrices.
MSTRS
Compute various stress criteria in multidimensional scaling.
L17......... Statistical data sets
GDATA
Retrieve a commonly analyzed data set.
N............ DATA HANDLING (search also class L2)
N1.......... Input, output
PGOPT
Set or retrieve page width and length for printing.
WRIRL
Print an integer rectangular matrix with a given format and labels.
WRIRN
Print an integer rectangular matrix with integer row and column labels.
WROPT
Set or retrieve an option for printing a matrix.
WRRRL
Print a real rectangular matrix with a given format and labels.
WRRRN
Print a real rectangular matrix with integer row and column labels.
N3.......... Character manipulation
ACHAR
Return a character given its ASCII value.
CVTSI
Convert a character string containing an integer number into the corresponding integer form.
IACHAR
Return the integer ASCII value of a character argument.
ICASE
Return the ASCII value of a character converted to uppercase.
IICSR
Compare two character strings using the ASCII collating sequence without regard to case.
IIDEX
Determine the position in a string at which a given character sequence begins without regard to case.
N5.......... Searching
N5a........ Extreme value
EQTIL
Compute empirical quantiles.
ORDST
Determine order statistics.
N5b........ Insertion position
ISRCH
Search a sorted integer vector for a given integer and return its index.
SRCH
Search a sorted vector for a given scalar and return its index.
SSRCH
Search a character vector, sorted in ascending ASCII order, for a given string and return its index.
N5c........ On a key
IIDEX
Determine the position in a string at which a given character sequence begins without regard to case.
ISRCH
Search a sorted integer vector for a given integer and return its index.
SRCH
Search a sorted vector for a given scalar and return its index.
SSRCH
Search a character vector, sorted in ascending ASCII order, for a given string and return its index.
N6.......... Sorting
N6a........ Internal
N6a1...... Passive (i.e., construct pointer array, rank)
N6a1a.... Integer
SVIGP
Sort an integer array by algebraic value and return the permutations.
N6a1b.... Real
RANKS
Compute the ranks, normal scores, or exponential scores for a vector of observations.
SCOLR
Sort columns of a real rectangular matrix using keys in rows.
SROWR
Sort rows of a real rectangular matrix using keys in columns.
SVRGP
Sort a real array by algebraic value and return the permutations.
N6a2...... Active
N6a2a.... Integer
SVIGN
Sort an integer array by algebraic value.
SVIGP
Sort an integer array by algebraically increasing value and return the permutation that rearranges the array.
N6a2b.... Real
SCOLR
Sort columns of a real rectangular matrix using keys in rows.
SROWR
Sort rows of a real rectangular matrix using keys in columns.
SVRGN
Sort a real array by algebraic value.
SVRGP
Sort a real array by algebraic value and return the permutations.
N8.......... Permuting
MVNAN
Move any rows of a matrix with the IMSL missing value code NaN (not a number) in the specified columns to the last rows of the matrix.
PERMA
Permute the rows or columns of a matrix.
PERMU
Rearrange the elements of an array as specified by a permutation.
RORDM
Reorder rows and columns of a symmetric matrix.
Q............ GRAPHICS (search also classes L3)
BOXP
Print boxplots for one or more samples.
CDF2P
Print a plot of two sample cumulative distribution functions.
CDFP
Print a sample cumulative distribution function (CDF), a theoretical CDF, and confidence band information.
HHSTP
Print a horizontal histogram.
PLOTP
Print a plot of up to ten sets of points.
PROBP
Print a probability plot.
SCTP
Print a scatterplot of several groups of data.
STMLP
Print a stem-and-leaf plot.
TREEP
Print a binary tree.
VHS2P
Print a vertical histogram with every bar subdivided into two parts.
VHSTP
Print a vertical histogram.
R............ SERVICE ROUTINES
IDYWK
Compute the day of the week for a given date.
NDAYS
Compute the number of days from January 1, 1900, to the given date.
NDYIN
Give the date corresponding to the number of days since January 1, 1900.
TDATE
Get today’s date.
TIMDY
Get time of day.
VERSL
Obtain STAT/LIBRARY-related version, system and license numbers.
R1.......... Machine-dependent constants
AMACH
Retrieve machine constants.
IFNAN Check if a floating-point number is NaN (not a number).
IMACH Retrieve integer machine constants.
UMACH Set or retrieve input or output device unit numbers.
R3.......... Error handling
R3b........ Set unit number for error messages
UMACH Set or retrieve input or output device unit numbers.
R3c........ Other utilities
ERSET Set error handler default print and stop actions.
IERCD Retrieve the code for an informational error.
N1RTY Retrieve an error type for the most recently called IMSL routine.
S............. SOFTWARE DEVELOPMENT TOOLS
CPSEC Return CPU time used in seconds.
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