IMSL STAT/LIBRARY

Appendix B: Alphabetical Summary of Routines

IMSL STAT/LIBRARY

Function Purpose Statement

ABALD	Analyzes a balanced complete experimental design for a fixed, random, or mixed model.
ABIBD	Analyzes a balanced incomplete block design or a balanced lattice design.
ACF	Computes the sample autocorrelation function of a stationary time series.
ACHAR	Returns a character given its ASCII value
ACTBL	Produces population and cohort life tables.
AKS1DF	Evaluates the cumulative distribution function of the one-sided Kolmogorov-Smirnov goodness-of-fit D+ or D− test statistic based on continuous data for one sample.
AKS2DF	Evaluates the cumulative distribution function of the Kolmogorov-Smirnov goodness-of-fit D test statistic based on continuous data for two samples
ALATN	Analyzes a Latin square design.
ALNDF	Evaluates the lognormal cumulative probability distribution function.
ALNIN	This function evaluates the inverse of the lognormal cumulative probability distribution function.
ALNPR	Evaluates the lognormal probability density function.
AMACH	Retrieves machine constants.
AMILLR	Evaluates Mill's ratio (the ratio of the ordinate to the upper tail area of the standardized normal distribution).
ANEST	Analyzes a completely nested random model with possibly unequal numbers in the subgroups.
ANORPR	Evaluates the normal probability density function.
ANORDF	Evaluates the standard normal (Gaussian) cumulative distribution function.
ANORIN	Evaluates the inverse of the standard normal (Gaussian) cumulative distribution function.
ANWAY	Analyzes a balanced n-way classification model with fixed effects.
AONEC	Analyzes a one-way classification model with covariates.
AONEW	Analyzes a one-way classification model.
ARMA_SPEC	Calculates the rational power spectrum for an ARMA model.
ARMME	Computes method of moments estimates of the autoregressive parameters of an ARMA model.
ATWOB	Analyzes a randomized block design or a two-way balanced design.
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_UNI_AR	Automatic selection and fitting of a multivariate autoregressive time series model.

BAY_SEA	Allows for a decomposition of a time series into trend, seasonal, and an error component.
BCTR	Performs a forward or an inverse Box-Cox (power) transformation.
BETDF	Evaluates the beta cumulative distribution function.
BETIN	Evaluates the inverse of the beta cumulative distribution function.
BETPR	Evaluates the beta probability density function.
BHAKV	Performs a Bhapkar V test.
BINDF	Evaluates the binomial cumulative distribution function.
BINES	Estimates the parameter p of the binomial distribution.
BINPR	Evaluates the binomial probability density function.
BNRDF	Evaluates the bivariate normal cumulative distribution function.
BOXP	Prints boxplots for one or more samples.
BSCAT	Computes the biserial correlation coefficient for a dichotomous variable and a classification variable.
BSPBS	Computes the biserial and point-biserial correlation coefficients for a dichotomous variable and a numerically measurable classification variable.

CANCR	Performs canonical correlation analysis from a data matrix.
CANVC	Performs canonical correlation analysis from a variance-covariance matrix or a correlation matrix.
CCF	Computes the sample cross-correlation function of two stationary time series.
CDF2P	Prints a plot of two sample cumulative distribution functions.
CDFP	Prints a sample cumulative distribution function (CDF), a theoretical CDF, and confidence band information.
CDIST	Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.
CESTI	Constructs an equivalent completely testable multivariate general linear hypothesis HBU = G from a partially testable hypothesis HpBU = Gp.
CHFAC	Computes an upper triangular factorization of a real symmetric nonnegative definite matrix.
CHIDF	Evaluates the chi-squared cumulative distribution function.
CHIGF	Performs a chi-squared goodness-of-fit test.
CHIIN	Evaluates the inverse of the chi-squared cumulative distribution function.
CHIPR	Evaluates the chi-squared probability density function.
CIDMS	Computes a confidence interval on a variance component estimated as proportional to the difference in two mean squares in a balanced complete experimental design.
CLINK	Performs a hierarchical cluster analysis given a distance matrix.
CNCRD	Calculates and tests the significance of the Kendall coefficient of concordance.
CNUMB	Computes cluster membership for a hierarchical cluster tree.
CORVC	Computes the variance-covariance or correlation matrix.
COVPL	Computes a pooled variance-covariance matrix from the observations.
CPFFT	Computes the cross periodogram of two stationary time series using a fast Fourier transform.
CPSEC	Returns CPU time used in seconds.
CSNDF	Evaluates the noncentral chi-squared cumulative distribution function.
CSNIN	Evaluates the inverse of the noncentral chi-squared cumulative function.
CSSWD	Estimates the nonnormalized cross-spectral density of two stationary time series using a spectral window given the time series data.
CSSWP	Estimates the nonnormalized cross-spectral density of two stationary time series using a spectral window given the spectral densities and cross periodogram.
CSTAT	Computes cell frequencies, cell means, and cell sums of squares for multivariate data.
CSWED	Estimates the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the time series data.
CSWEP	Estimates the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the spectral densities and cross periodogram.
CTASC	Computes partial association statistics for log-linear models in a multidimensional contingency table.
CTCHI	Performs a chi-squared analysis of a two-way contingency table.
CTEPR	Computes Fisher's exact test probability and a hybrid approximation to the Fisher exact test probability for a contingency table using the network algorithm.
CTGLM	Analyzes categorical data using logistic, Probit, Poisson, and other generalized linear models.
CTLLN	Computes model estimates and associated statistics for a hierarchical log-linear model.
CTPAR	Computes model estimates and covariances in a fitted log-linear model.
CTPRB	Computes exact probabilities in a two-way contingency table.
CTRAN	Performs generalized Mantel-Haenszel tests in a stratified contingency table.
CTRHO	Estimates the bivariate normal correlation coefficient using a contingency table.
CTRST	Computes contrast estimates and sums of squares.
CTSTP	Builds hierarchical log-linear models using forward selection, backward selection, or stepwise selection.
CTTWO	Performs a chi-squared analysis of a 2 by 2 contingency table.
CTWLS	Performs a generalized linear least squares analysis of transformed probabilities in a two-dimensional contingency table.
CVTSI	Converts a character string containing an integer number into the corresponding integer form.

DCUBE	Performs a triplets test.
DESKN	Performs nonparametric probability density function estimation by the kernel method.
DESPL	Performs nonparametric probability density function estimation by the penalized likelihood method.
DESPT	Estimates a probability density function at specified points using linear or cubic interpolation.
DIFF	Difference a time series.
DIRIC	Computes the Dirichlet kernel.
DMACH	See AMACH.
DMSCR	Uses Fisher's linear discriminant analysis method to reduce the number of variables.
DNFFT	Computes Gaussian kernel estimates of a univariate density via the fast Fourier transform over a fixed interval.
DSCRM	Performs a linear or a quadratic discriminant function analysis among several known groups.
DSQAR	Performs a D-square test.

ENOS	Evaluates the expected value of a normal order statistic.
EQTIL	Computes empirical quantiles.
ERSET	Sets error handler default print and stop actions.
ESTIMATE_MISSING	Estimates missing values in a time series.
EXPDF	Evaluates the exponential cumulative distribution function.
EXPIN	Evaluates the inverse of the exponential cumulative distribution function.
EXPPR	This function evaluates the exponential probability density function.
EXVDF	Evaluates the extreme value cumulative distribution function.
EXVIN	Evaluates the inverse of the extreme value cumulative distribution function.
EXVPR	Evaluates the extreme value probability density function.

FACTR	Extracts initial factor-loading estimates in factor analysis.
FAURE_FREE	Frees the structure containing information about the Faure sequence.
FAURE_INIT	Computes a shuffled Faure sequence.
FAURE_NEXT	Shuffled Faure sequence initialization.
FCOEF	Computes a matrix of factor score coefficients for input to the following IMSL routine (FSCOR).
FDF	Evaluates the F cumulative distribution function.
FDOBL	Computes a direct oblimin rotation of a factor-loading matrix.
FEJER	Computes the Fejér kernel.
FGCRF	Computes direct oblique rotation according to a generalized fourth-degree polynomial criterion.
FHARR	Computes an oblique rotation of an unrotated factor-loading matrix using the Harris-Kaiser method.
FIMAG	Computes the image transformation matrix.
FIN	Evaluates the inverse of the F cumulative distribution function.
FOPCS	Computes an orthogonal Procrustes rotation of a factor-loading matrix using a target matrix.
FPR	Evaluates the F probability density function.
FPRMX	Computes an oblique Promax or Procrustes rotation of a factor-loading matrix using a target matrix, including pivot and power vector options.
FRDMN	Performs Friedman's test for a randomized complete block design.
FREQ	Tallies multivariate observations into a multi-way frequency table.
FRESI	Computes commonalities and the standardized factor residual correlation matrix
FROTA	Computes an orthogonal rotation of a factor-loading matrix using a generalized orthomax criterion, including quartimax, varimax, and equamax rotations.
FRVAR	Computes the factor structures and the variance explained by each factor.
FSCOR	Computes a set of factor scores given the factor score coefficient matrix.

GAMDF	Evaluates the gamma cumulative distribution function.
GAMIN	Evaluates the inverse of the gamma cumulative distribution function.
GAMPR	Evaluates the gamma probability density function.
GARCH	Computes estimates of the parameters of a GARCH (p,q) model.
GCDF	Evaluates a general continuous cumulative distribution function given ordinates of the density.
GCIN	Evaluates the inverse of a general continuous cumulative distribution function given ordinates of the density.
GCLAS	Gets the unique values of each classification variable.
GCSCP	Generates centered variables, squares, and crossproducts.
GDATA	Retrieves a commonly analyzed data set.
GEODF	Evaluates the geometric cumulative probability distribution function.
GEOIN	Evaluates the inverse of the geometric cumulative probability distribution function.
GEOPR	Evaluates the geometric probability density function.
GFNIN	Evaluates the inverse of a general continuous cumulative distribution function given in a subprogram.
GIRTS	Solves a triangular (possibly singular) set of linear systems and/or compute a generalized inverse of an upper triangular matrix.
GRGLM	Generates regressors for a general linear model.
GRPES	Computes basic statistics from grouped data.
GSWEP	Performs a generalized sweep of a row of a nonnegative definite matrix.

HAZEZ	Performs nonparametric hazard rate estimation using kernel functions. Easy-to-use version of the previous IMSL subroutine (HAZRD).
HAZRD	Performs nonparametric hazard rate estimation using kernel functions and quasi-likelihoods.
HAZST	Performs hazard rate estimation over a grid of points using a kernel function.
HHSTP	Prints a horizontal histogram
HYPDF	Evaluates the hypergeometric cumulative distribution function.
HYPPR	Evaluates the hypergeometric probability function.

IACHAR	Returns the integer ASCII value of a character argument.
ICASE	Returns the ASCII value of a character converted to uppercase.
IDYWK	Computes the day of the week for a given date.
IERCD and N1RTY	Retrieves the code for an informational error.
IFNAN(X)	Checks if a floating-point number is NaN (not a number).
IICSR	Compares two character strings using the ASCII collating sequence without regard to case.
IIDEX	Determines the position in a string at which a given character sequence begins without regard to case.
IMACH	Retrieves integer machine constants.
INCLD	Performs an includance test.
IRNSE	Computes estimates of the impulse response weights and noise series of a univariate transfer function model.
ISRCH	Searches a sorted integer vector for a given integer and returns its index.

KALMN	Performs Kalman filtering and evaluate the likelihood function for the state-space model.
KAPMR	Computes Kaplan-Meier estimates of survival probabilities in stratified samples.
KENDL	Computes and tests Kendall's rank correlation coefficient.
KENDP	Computes the frequency distribution of the total score in Kendall's rank correlation coefficient.
KMEAN	Performs a K-means (centroid) cluster analysis.
KPRIN	Maximum likelihood or least-squares estimates for principle components from one or more matrices.
KRSKL	Performs a Kruskal-Wallis test for identical population medians.
KSONE	Performs a Kolmogorov-Smirnov one-sample test for continuous distributions.
KSTWO	Performs a Kolmogorov-Smirnov two-sample test.
KTBLE	Prints Kaplan-Meier estimates of survival probabilities in stratified samples.
KTRND	Performs a k-sample trends test against ordered alternatives.

LETTR	Produces a letter value summary.
LILLF	Performs Lilliefors test for an exponential or normal distribution.
LOFCF	Performs lack-of-fit test for a univariate time series or transfers function given the appropriate correlation function.

MAMME	Computes method of moments estimates of the moving average parameters of an ARMA model.
MAX_ARMA	Exacts maximum likelihood estimation of the parameters in a univariate ARMA (auto-regressive, moving average) time series model.
MCCF	Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.
MCHOL	Computes an upper triangular factorization of a real symmetric matrix A plus a diagonal matrix D, where D is determined sequentially during the Cholesky factorization in order to make A + D nonnegative definite.
MEDPL	Computes a median polish of a two-way table.
MLSE	Computes least squares estimates of a linear regression model for a multichannel time series with a specified base channel.
MSDBL	Obtains normalized product-moment (double centered) matrices from dissimilarity matrices.
MSDST	Computes distances in a multidimensional scaling model.
MSIDV	Performs individual-differences multidimensional scaling for metric data using alternating least squares.
MSINI	Computes initial estimates in multidimensional scaling models.
MSSTN	Transforms dissimilarity/similarity matrices and replace missing values by estimates to obtain standardized dissimilarity matrices.
MSTRS	Computes various stress criteria in multidimensional scaling.
MVIND	Computes a test for the independence of k sets of multivariate normal variables.
MVMMT	Computes Mardia's multivariate measures of skewness and kurtosis and tests for multivariate normality.
MVNAN	Moves 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.
MWFE	Computes least squares estimates of the multichannel Wiener filter coefficients for two mutually stationary multichannel time series.

IERCD and N1RTY	Retrieves an error type for the most recently called IMSL routine.
NCTRD	Performs the Noether test for cyclical trend.
NDAYS	Computes the number of days from January 1, 1900, to the given date.
NDYIN	Gives the date corresponding to the number of days since January 1, 1900.
NGHBR	Searches a k-d tree for the k nearest neighbors of a key.
NNBRD	Performs a k nearest neighbor discrimination.
NRCES	Computes maximum likelihood estimates of the mean and variance from grouped and/or censored normal data.
NSBJF	Computes Box-Jenkins forecasts and their associated probability limits for a nonseasonal ARMA model.
NSLSE	Computes least squares estimates of parameters for a nonseasonal ARMA model.
NSPE	Computes preliminary estimates of the autoregressive and moving average parameters of an ARMA model.
NTIES	Computes tie statistics for a sample of observations.

OPOLY	Generates orthogonal polynomials with respect to x values and specified weights.
OPT_DES	Allows for multiple channels for both the controlled and manipulated variables.
ORDST	Determines order statistics.
OWFRQ	Tallies observations into a one-way frequency table.

PACF	Computes the sample partial autocorrelation function of a stationary time series.
PAIRS	Performs a pairs test.
PCORR	Computes partial correlations or covariances from the covariance or correlation matrix.
PERMA	Permutes the rows or columns of a matrix.
PERMU	Rearranges the elements of an array as specified by a permutation.
PFFT	Computes the periodogram of a stationary time series using a fast Fourier transform.
PGOPT	Sets or retrieves page width and length for printing.
PHGLM	Analyzes time event data via the proportional hazards model.
PLOTP	Prints a plot of up to ten sets of points.
POIDF	Evaluates the Poisson cumulative distribution function.
POIES	Estimates the parameter of the Poisson distribution.
POIPR	Evaluates the Poisson probability density function.
PRINC	Computes principal components from a variance-covariance matrix or a correlation matrix.
PROBP	Prints a probability plot.
PRPFT	Performs iterative proportional fitting of a contingency table using a loglinear model.

QTEST	Performs a Cochran Q test for related observations.
QUADT	Forms a k-d tree.

RALDF	Evaluates the Rayleigh cumulative distribution function.
RALIN	Evaluates the inverse of the Rayleigh cumulative distribution function.
RALPR	Evaluates the Rayleigh probability density function.
RANKS	Computes the ranks, normal scores, or exponential scores for a vector of observations.
RBCOV	Computes a robust estimate of a covariance matrix and mean vector.
RBEST	Selects the best multiple linear regression models.
RCASE	Computes case statistics and diagnostics given data points, coefficient estimates, and the R matrix for a fitted general linear model.
RCASP	Computes case statistics for a polynomial regression model given the fit based on orthogonal polynomials.
RCOMP	Generates an orthogonal central composite design.
RCOV	Fits a multiple linear regression model given the variance-covariance matrix.
RCOVB	Computes the estimated variance-covariance matrix of the estimated regression coefficients given the R matrix.
RCURV	Fits a polynomial curve using least squares.
RFORP	Fits an orthogonal polynomial regression model.
RGIVN	Fits a multivariate linear regression model via fast Givens transformations.
RGLM	Fits a multivariate general linear model.
RHPSS	Computes 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	Performs 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.
RINCF	Performs response control given a fitted simple linear regression model.
RINPF	Performs inverse prediction given a fitted simple linear regression model.
RLAV	Fits a multiple linear regression model using the least absolute values criterion.
RLEQU	Fits 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.
RLINE	Fits a line to a set of data points using least squares.
RLLP	Fits a multiple linear regression model using the Lp norm criterion.
RLMV	Fits a multiple linear regression model using the minimax criterion.
RLOFE	Computes a lack-of-fit test based on exact replicates for a fitted regression model.
RLOFN	Computes a lack-of-fit test based on near replicates for a fitted regression model.
RLSE	Fits a multiple linear regression model using least squares.
RNARM	Generates a time series from a specified ARMA model.
RNBET	Generates pseudorandom numbers from a beta distribution.
RNBIN	Generates pseudorandom numbers from a binomial distribution.
RNCHI	Generates pseudorandom numbers from a chi-squared distribution.
RNCHY	Generates pseudorandom numbers from a Cauchy distribution.
RNCOR	Generates a pseudorandom orthogonal matrix or a correlation matrix.
RNDAT	Generates pseudorandom numbers from a multivariate distribution determined from a given sample.
RNEXP	Generates pseudorandom numbers from a standard exponential distribution.
RNEXT	Generates pseudorandom numbers from a mixture of two exponential distributions.
RNEXV	Generates pseudorandom numbers from an extreme value distribution.
RNFDF	Generates pseudorandom numbers from the F distribution.
RNGAM	Generates pseudorandom numbers from a standard gamma distribution.
RNGCS	Sets up table to generate pseudorandom numbers from a general continuous distribution.
RNGCT	Generates pseudorandom numbers from a general continuous distribution.
RNGDA	Generates pseudorandom numbers from a general discrete distribution using an alias method.
RNGDS	Sets up table to generate pseudorandom numbers from a general discrete distribution.
RNGDT	Generates pseudorandom numbers from a general discrete distribution using a table lookup method.
RNGEF (See RNG in Chapter 18 )	Retrieves the current value of the array used in the IMSL GFSR random number generator.
RNGEO	Generates pseudorandom numbers from a geometric distribution.
RNGES (See RNG in Chapter 18 )	Retrieves the current value of the table in the IMSL random number generators that use shuffling.
RNGET (See RNG in Chapter 18 )	Retrieves the current value of the seed used in the IMSL random number generators.
RNHYP	Generates pseudorandom numbers from a hypergeometric distribution.
RNIN32	Initializes the 32-bit Merseene Twister generator using an array.
RNGE32	Retrieves the current table used in the 32-bit Mersenne Twister generator.
RNSE32	Sets the current table used in the 32-bit Mersenne Twister generator.
RNIN64	Initializes the 32-bit Merseene Twister generator using an array.
RNGE64	Retrieves the current table used in the 64-bit Mersenne Twister generator
RNSE64	Sets the current table used in the 64-bit Mersenne Twister generator.
RNISD (See RNG in Chapter 18 )	Determines 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).
RNKSM	Performs the Wilcoxon rank sum test.
RNLGR	Generates pseudorandom numbers from a logarithmic distribution.
RNLIN	Fits a nonlinear regression model.
RNLNL	Generates pseudorandom numbers from a lognormal distribution.
RNMTN	Generates pseudorandom numbers from a multinomial distribution.
RNMVN	Generates pseudorandom numbers from a multivariate normal distribution.
RNNBN	Generates pseudorandom numbers from a negative binomial distribution.
RNNOA	Generates pseudorandom numbers from a standard normal distribution using an acceptance/rejection method.
RNNOF	Generates a pseudorandom number from a standard normal distribution.
RNNOR	Generates pseudorandom numbers from a standard normal distribution using an inverse CDF method.
RNNOS	Generates pseudorandom order statistics from a standard normal distribution.
RNNPP	Generates pseudorandom numbers from a nonhomogeneous Poisson process.
RNOPG (See RNG in Chapter 18)	Retrieves the indicator of the type of uniform random number generator.
RNOPT (See RNG in Chapter 18)	Selects the uniform (0, 1) multiplicative congruential pseudorandom number generator.
RNPER	Generates a pseudorandom permutation.
RNPOI	Generates pseudorandom numbers from a Poisson distribution.
RNRAL	Generates pseudorandom numbers from a Rayleigh distribution.
RNSEF (See RNG in Chapter 18)	Initializes the array used in the IMSL GFSR random number generator.
RNSES (See RNG in Chapter 18)	Initializes the table in the IMSL random number generators that use shuffling.
RNSET (See RNG in Chapter 18)	Initializes a random seed for use in the IMSL randomnumber generators.
RNSPH	Generates pseudorandom points on a unit circle or K-dimensional sphere.
RNSRI	Generates a simple pseudorandom sample of indices.
RNSRS	Generates a simple pseudorandom sample from a finite population.
RNSTA	Generates pseudorandom numbers from a stable distribution.
RNSTT	Generates pseudorandom numbers from a Student's t distribution.
RNTAB	Generates a pseudorandom two-way table.
RNTRI	Generates pseudorandom numbers from a triangular distribution on the interval (0,1).
RNUN	Generates pseudorandom numbers from a uniform (0,1) distribution.
RNUND	Generates pseudorandom numbers from a discrete uniform distribution.
RNUNF	Generates a pseudorandom number from a uniform (0, 1) distribution.
RNUNO	Generates pseudorandom order statistics from a uniform (0, 1) distribution.
RNVMS	Generates pseudorandom numbers from a von Mises distribution.
RNWIB	Generates pseudorandom numbers from a Weibull distribution.
RONE	Analyzes a simple linear regression model.
RORDM	Reorders rows and columns of a symmetric matrix.
ROREX	Reorders the responses from a balanced complete experimental design.
ROTIN	Computes diagnostics for detection of outliers and influential data points given residuals and the R matrix for a fitted general linear model.
RPOLY	Analyzes a polynomial regression model.
RSTAP	Computes summary statistics for a polynomial regression model given the fit based on orthogonal polynomials.
RSTAT	Computes statistics related to a regression fit given the coefficient estimates and the R matrix.
RSTEP	Builds multiple linear regression models using forward selection, backward selection, or stepwise selection.
RSUBM	Retrieves a symmetric submatrix from a symmetric matrix.
RUNS	Performs a runs up test.

SCIPM	Computes simultaneous confidence intervals on all pairwise differences of means.
SCOLR	Sorts columns of a real rectangular matrix using keys in rows.
SCTP	Prints a scatterplot of several groups of data.
SDPLC	Performs the Cox and Stuart sign test for trends in dispersion and location.
SEASONAL_FIT	Determines an optimal differencing for seasonal adjustments of a time series.
SIGNT	Performs a sign test of the hypothesis that a given value is a specified quantile of a distribution.
SMPPR	Computes statistics for inferences regarding the population proportion and total, given proportion data from a simple random sample.
SMPPS	Computes statistics for inferences regarding the population proportion and total, given proportion data from a stratified random sample.
SMPRR	Computes 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	Computes statistics for inferences regarding the population mean and total using ratio or regression estimation, given continuous data from a stratified random sample.
SMPSC	Computes statistics for inferences regarding the population mean and total using single-stage cluster sampling with continuous data.
SMPSR	Computes statistics for inferences regarding the population mean and total, given data from a simple random sample.
SMPSS	Computes statistics for inferences regarding the population mean and total, given data from a stratified random sample.
SMPST	Computes statistics for inferences regarding the population mean and total, given continuous data from a two-stage sample with equisized primary units.
SNKMC	Performs Student-Newman-Keuls multiple comparison test.
SNRNK	Performs a Wilcoxon signed rank test.
SPWF	Computes the Wiener forecast operator for a stationary stochastic process.
SPWLK	Performs a Shapiro-Wilk W-test for normality.
SRCH	Searches a sorted vector for a given scalar and return its index.
SROWR	Sorts rows of a real rectangular matrix using keys in columns.
SSRCH	Searches a character vector, sorted in ascending ASCII order, for a given string and return its index.
SSWD	Estimates the nonnormalized spectral density of a stationary time series using a spectral window given the time series data.
SSWP	Estimates the nonnormalized spectral density of a stationary time series using a spectral window given the periodogram.
STBLE	Estimates survival probabilities and hazard rates for various parametric models.
STMLP	Prints a stem-and-leaf plot.
SVGLM	Analyzes censored survival data using a generalized linear model.
SVIGN	Sorts an integer array by algebraic value.
SVIGP	Sorts an integer array by algebraic value and returns the permutations.
SVRGN	Sorts a real array by algebraic value.
SVRGP	Sorts a real array by algebraic value and returns the permutations.
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.

TCSCP	Transforms coefficients from a quadratic regression model generated from squares and crossproducts of centered variables to a model using uncentered variables.
TDATE	Gets today's date.
TDF	Evaluates the Student's t cumulative distribution function.
TETCC	Categorizes bivariate data and compute the tetrachoric correlation coefficient.
TFPE	Computes preliminary estimates of parameters for a univariate transfer function model.
TIMDY	Gets time of day.
TIN	Evaluates the inverse of the Student's t distribution function.
TNDF	Evaluates the noncentral Student's t cumulative distribution function.
TNIN	Evaluates the inverse of the noncentral Student's t cumulative distribution function.
TPR	This function evaluates the Student's t probability density function.
TREEP	Prints a binary tree.
TRNBL	Computes Turnbull's generalized Kaplan-Meier estimates of survival probabilities in samples with interval censoring.
TWFRQ	Tallies observations into a two-way frequency table.
TWOMV	Computes statistics for mean and variance inferences using samples from two normal populations.

UMACH	Sets or retrieves input or output device unit numbers.
UNDDF	Evaluates the discrete uniform cumulative distribution function.
UNDF	Evaluates the uniform cumulative distribution function.
UNDIN	Evaluates the inverse of the discrete uniform cumulative distribution function.
UNDPR	Evaluates the discrete uniform probability density function.
UNIN	Evaluates the inverse of the uniform cumulative distribution function.
UNPR	Evaluates the uniform probability density function.
UVSTA	Computes basic univariate statistics.

VERSL	Obtains STAT/LIBRARY-related version, system and license numbers.
VHS2P	Prints a vertical histogram with every bar subdivided into two parts.
VHSTP	Prints a vertical histogram.

WBLDF	Evaluates the Weibull cumulative distribution function.
WBLIN	Evaluates the inverse of the Weibull cumulative distribution function.
WBLPR	Evaluates the Weibull probability density function.
WRIRL	Prints an integer rectangular matrix with a given format and labels.
WRIRN	Prints an integer rectangular matrix with integer row and column labels.
WROPT	Sets or retrieves an option for printing a matrix.
WRRRL	Prints a real rectangular matrix with a given format and labels.
WRRRN	Prints a real rectangular matrix with integer row and column labels.

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