[A] [B] [C] [D] [E] [F] [G] [H] [ I ] [J] [K] [L] [M]
[N]
[O] [P]
[Q] [R]
[S] [T]
[U] [V]
[W] [X] [Y] [Z]
Function Purpose Statement
A
Analyzes a balanced complete experimental design for a fixed, random, or mixed model. | |
Analyzes a balanced incomplete block design or a balanced lattice design. | |
Computes the sample autocorrelation function of a stationary time series. | |
Returns a character given its ASCII value | |
Produces population and cohort life tables. | |
Performs an Anderson-Darling test for normality. | |
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. | |
Evaluates the cumulative distribution function of the Kolmogorov-Smirnov goodness-of-fit D test statistic based on continuous data for two samples | |
Analyzes a Latin square design. | |
Evaluates the lognormal cumulative probability distribution function. | |
This function evaluates the inverse of the lognormal cumulative probability distribution function. | |
Evaluates the lognormal probability density function. | |
Retrieves machine constants. | |
Evaluates Mill's ratio (the ratio of the ordinate to the upper tail area of the standardized normal distribution). | |
Analyzes a completely nested random model with possibly unequal numbers in the subgroups. | |
Evaluates the normal probability density function. | |
Evaluates the standard normal (Gaussian) cumulative distribution function. | |
Evaluates the inverse of the standard normal (Gaussian) cumulative distribution function. | |
Analyzes a balanced n-way classification model with fixed effects. | |
Analyzes a one-way classification model with covariates. | |
Analyzes a one-way classification model. | |
Calculates the rational power spectrum for an ARMA model. | |
Computes method of moments estimates of the autoregressive parameters of an ARMA model. | |
Analyzes a randomized block design or a two-way balanced design. | |
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. | |
Automatic selection and fitting of a multivariate autoregressive time series model using Akaike’s Multivariate Final Prediction Error (MFPE) criteria. | |
Automatic selection and fitting of a univariate autoregressive time series model using Akaike’s Final Prediction Error (FPE) criteria. | |
Automatic selection and fitting of a multivariate autoregressive time series model. | |
Estimates structural breaks in non-stationary univariate time series. | |
Automatic selection and fitting of a multivariate autoregressive time series model. |
B
Allows for a decomposition of a time series into trend, seasonal, and an error component. | |
Performs a forward or an inverse Box-Cox (power) transformation. | |
Evaluates the beta cumulative distribution function. | |
Evaluates the inverse of the beta cumulative distribution function. | |
This function evaluates the noncentral beta cumulative distribution function (CDF) . | |
This function evaluates the inverse of the noncentral beta cumulative distribution function (CDF). | |
This function evaluates the noncentral beta probability density function. | |
Evaluates the beta probability density function. | |
Performs a Bhapkar V test. | |
Evaluates the binomial cumulative distribution function. | |
Estimates the parameter p of the binomial distribution. | |
Evaluates the binomial probability density function. | |
Evaluates the bivariate normal cumulative distribution function. | |
Prints boxplots for one or more samples. | |
Computes the biserial correlation coefficient for a dichotomous variable and a classification variable. | |
Computes the biserial and point-biserial correlation coefficients for a dichotomous variable and a numerically measurable classification variable. |
C
Given an input array of deviate values, generates a canonical correlation array. | |
Performs canonical correlation analysis from a data matrix. | |
Performs canonical correlation analysis from a variance-covariance matrix or a correlation matrix. | |
Computes the sample cross-correlation function of two stationary time series. | |
Prints a plot of two sample cumulative distribution functions. | |
Prints a sample cumulative distribution function (CDF), a theoretical CDF, and confidence band information. | |
Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix. | |
Constructs an equivalent completely testable multivariate general linear hypothesis HBU = G from a partially testable hypothesis HpBU = Gp. | |
Computes an upper triangular factorization of a real symmetric nonnegative definite matrix. | |
Evaluates the chi-squared cumulative distribution function. | |
Performs a chi-squared goodness-of-fit test. | |
Evaluates the inverse of the chi-squared cumulative distribution function. | |
Evaluates the chi-squared probability density function. | |
Computes a confidence interval on a variance component estimated as proportional to the difference in two mean squares in a balanced complete experimental design. | |
Performs a hierarchical cluster analysis given a distance matrix. | |
Calculates and tests the significance of the Kendall coefficient of concordance. | |
Computes cluster membership for a hierarchical cluster tree. | |
Computes the variance-covariance or correlation matrix. | |
Computes a pooled variance-covariance matrix from the observations. | |
Computes the cross periodogram of two stationary time series using a fast Fourier transform. | |
Returns CPU time used in seconds. | |
Evaluates the noncentral chi-squared cumulative distribution function. | |
Evaluates the inverse of the noncentral chi-squared cumulative function. | |
This function evaluates the noncentral chi-squared probability density function. | |
Estimates the nonnormalized cross-spectral density of two stationary time series using a spectral window given the time series data. | |
Estimates the nonnormalized cross-spectral density of two stationary time series using a spectral window given the spectral densities and cross periodogram. | |
Computes cell frequencies, cell means, and cell sums of squares for multivariate data. | |
Estimates the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the time series data. | |
Estimates the nonnormalized cross-spectral density of two stationary time series using a weighted cross periodogram given the spectral densities and cross periodogram. | |
Computes partial association statistics for log-linear models in a multidimensional contingency table. | |
Performs a chi-squared analysis of a two-way contingency table. | |
Computes Fisher’s exact test probability and a hybrid approximation to the Fisher exact test probability for a contingency table using the network algorithm. | |
Analyzes categorical data using logistic, Probit, Poisson, and other generalized linear models. | |
Computes model estimates and associated statistics for a hierarchical log-linear model. | |
Computes model estimates and covariances in a fitted log-linear model. | |
Computes exact probabilities in a two-way contingency table. | |
Performs generalized Mantel-Haenszel tests in a stratified contingency table. | |
Estimates the bivariate normal correlation coefficient using a contingency table. | |
Computes contrast estimates and sums of squares. | |
Builds hierarchical log-linear models using forward selection, backward selection, or stepwise selection. | |
Performs a chi-squared analysis of a 2 by 2 contingency table. | |
Performs a generalized linear least squares analysis of transformed probabilities in a two-dimensional contingency table. | |
Performs a Cramer-von Mises test for normality. | |
Converts a character string containing an integer number into the corresponding integer form. |
D
Performs a triplets test. | |
Performs nonparametric probability density function estimation by the kernel method. | |
Performs nonparametric probability density function estimation by the penalized likelihood method. | |
Estimates a probability density function at specified points using linear or cubic interpolation. | |
Difference a time series. | |
Computes the Dirichlet kernel. | |
See AMACH. | |
Uses Fisher’s linear discriminant analysis method to reduce the number of variables. | |
Computes Gaussian kernel estimates of a univariate density via the fast Fourier transform over a fixed interval. | |
Performs a linear or a quadratic discriminant function analysis among several known groups. | |
Performs a D-square test. |
E
Evaluates the expected value of a normal order statistic. | |
Computes empirical quantiles. | |
Sets error handler default print and stop actions. | |
Estimates missing values in a time series. | |
Evaluates the exponential cumulative distribution function. | |
Evaluates the inverse of the exponential cumulative distribution function. | |
This function evaluates the exponential probability density function. | |
Evaluates the extreme value cumulative distribution function. | |
Evaluates the inverse of the extreme value cumulative distribution function. | |
Evaluates the extreme value probability density function. |
F
Extracts initial factor-loading estimates in factor analysis. | |
Frees the structure containing information about the Faure sequence. | |
Computes a shuffled Faure sequence. | |
Shuffled Faure sequence initialization. | |
Computes a matrix of factor score coefficients for input to the following IMSL routine (FSCOR). | |
Evaluates the F cumulative distribution function. | |
Computes a direct oblimin rotation of a factor-loading matrix. | |
Computes the Fejér kernel. | |
Computes direct oblique rotation according to a generalized fourth-degree polynomial criterion. | |
Computes an oblique rotation of an unrotated factor-loading matrix using the Harris-Kaiser method. | |
Computes the image transformation matrix. | |
Evaluates the inverse of the F cumulative distribution function. | |
Noncentral F cumulative distribution function. | |
This function evaluates the inverse of the noncentral F cumulative distribution function (CDF). | |
This function evaluates the noncentral F cumulative distribution function (CDF). | |
Computes an orthogonal Procrustes rotation of a factor-loading matrix using a target matrix. | |
Evaluates the F probability density function. | |
Computes an oblique Promax or Procrustes rotation of a factor-loading matrix using a target matrix, including pivot and power vector options. | |
Performs Friedman’s test for a randomized complete block design. | |
Tallies multivariate observations into a multi-way frequency table. | |
Computes commonalities and the standardized factor residual correlation matrix | |
Computes an orthogonal rotation of a factor-loading matrix using a generalized orthomax criterion, including quartimax, varimax, and equamax rotations. | |
Computes the factor structures and the variance explained by each factor. | |
Computes a set of factor scores given the factor score coefficient matrix. |
G
Evaluates the gamma cumulative distribution function. | |
Evaluates the inverse of the gamma cumulative distribution function. | |
Evaluates the gamma probability density function. | |
Computes estimates of the parameters of a GARCH (p,q) model. | |
Evaluates a general continuous cumulative distribution function given ordinates of the density. | |
Evaluates the inverse of a general continuous cumulative distribution function given ordinates of the density. | |
Gets the unique values of each classification variable. | |
Generates centered variables, squares, and crossproducts. | |
Retrieves a commonly analyzed data set. | |
Evaluates the geometric cumulative probability distribution function. | |
Evaluates the inverse of the geometric cumulative probability distribution function. | |
Evaluates the geometric probability density function. | |
Evaluates the inverse of a general continuous cumulative distribution function given in a subprogram. | |
Solves a triangular (possibly singular) set of linear systems and/or compute a generalized inverse of an upper triangular matrix. | |
Generates regressors for a general linear model. | |
Computes basic statistics from grouped data. | |
Performs a generalized sweep of a row of a nonnegative definite matrix. |
H
Performs nonparametric hazard rate estimation using kernel functions. Easy-to-use version of the previous IMSL subroutine (HAZRD). | |
Performs nonparametric hazard rate estimation using kernel functions and quasi-likelihoods. | |
Performs hazard rate estimation over a grid of points using a kernel function. | |
Prints a horizontal histogram | |
Evaluates the hypergeometric cumulative distribution function. | |
Evaluates the hypergeometric probability function. |
I
Returns the integer ASCII value of a character argument. | |
Returns the ASCII value of a character converted to uppercase. | |
Computes the day of the week for a given date. | |
Retrieves the code for an informational error. | |
Checks if a floating-point number is NaN (not a number). | |
Compares two character strings using the ASCII collating sequence without regard to case. | |
Determines the position in a string at which a given character sequence begins without regard to case. | |
Retrieves integer machine constants. | |
Performs an includance test. | |
Computes estimates of the impulse response weights and noise series of a univariate transfer function model. | |
Searches a sorted integer vector for a given integer and returns its index. |
K
Performs Kalman filtering and evaluate the likelihood function for the state-space model. | |
Computes Kaplan-Meier estimates of survival probabilities in stratified samples. | |
Computes and tests Kendall’s rank correlation coefficient. | |
Computes the frequency distribution of the total score in Kendall’s rank correlation coefficient. | |
Performs a K-means (centroid) cluster analysis. | |
Maximum likelihood or least-squares estimates for principle components from one or more matrices. | |
Performs a Kruskal-Wallis test for identical population medians. | |
Performs a Kolmogorov-Smirnov one-sample test for continuous distributions. | |
Performs a Kolmogorov-Smirnov two-sample test. | |
Prints Kaplan-Meier estimates of survival probabilities in stratified samples. | |
Performs a k-sample trends test against ordered alternatives. |
L
Produces a letter value summary. | |
Performs Lilliefors test for an exponential or normal distribution. | |
Performs lack-of-fit test for a univariate time series or transfers function given the appropriate correlation function. |
M
Computes method of moments estimates of the moving average parameters of an ARMA model. | |
Exacts maximum likelihood estimation of the parameters in a univariate ARMA (auto-regressive, moving average) time series model. | |
Computes the multichannel cross-correlation function of two mutually stationary multichannel time series. | |
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. | |
Computes a median polish of a two-way table. | |
Computes least squares estimates of a linear regression model for a multichannel time series with a specified base channel. | |
Calculates maximum likelihood estimates for the parameters of one of several univariate probability distributions. | |
Obtains normalized product-moment (double centered) matrices from dissimilarity matrices. | |
Computes distances in a multidimensional scaling model. | |
Performs individual-differences multidimensional scaling for metric data using alternating least squares. | |
Computes initial estimates in multidimensional scaling models. | |
Transforms dissimilarity/similarity matrices and replace missing values by estimates to obtain standardized dissimilarity matrices. | |
Computes various stress criteria in multidimensional scaling. | |
Computes a test for the independence of k sets of multivariate normal variables. | |
Computes Mardia’s multivariate measures of skewness and kurtosis and tests for multivariate normality. | |
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. | |
Computes least squares estimates of the multichannel Wiener filter coefficients for two mutually stationary multichannel time series. |
N
Retrieves an error type for the most recently called IMSL routine. | |
Performs the Noether test for cyclical trend. | |
Computes the number of days from January 1, 1900, to the given date. | |
Gives the date corresponding to the number of days since January 1, 1900. | |
Searches a k-d tree for the k nearest neighbors of a key. | |
Performs a k nearest neighbor discrimination. | |
Computes maximum likelihood estimates of the mean and variance from grouped and/or censored normal data. | |
Computes Box-Jenkins forecasts and their associated probability limits for a nonseasonal ARMA model. | |
Computes least squares estimates of parameters for a nonseasonal ARMA model. | |
Computes preliminary estimates of the autoregressive and moving average parameters of an ARMA model. | |
Computes tie statistics for a sample of observations. |
O
Generates orthogonal polynomials with respect to x values and specified weights. | |
Allows for multiple channels for both the controlled and manipulated variables. | |
Determines order statistics. | |
Tallies observations into a one-way frequency table. |
P
Computes the sample partial autocorrelation function of a stationary time series. | |
Performs a pairs test. | |
Computes partial correlations or covariances from the covariance or correlation matrix. | |
Permutes the rows or columns of a matrix. | |
Rearranges the elements of an array as specified by a permutation. | |
Computes the periodogram of a stationary time series using a fast Fourier transform. | |
Sets or retrieves page width and length for printing. | |
Analyzes time event data via the proportional hazards model. | |
Prints a plot of up to ten sets of points. | |
Performs partial least squares regression for one or more response variables and a set of one or more predictor variables. | |
Evaluates the Poisson cumulative distribution function. | |
Estimates the parameter of the Poisson distribution. | |
Evaluates the Poisson probability density function. | |
Computes principal components from a variance-covariance matrix or a correlation matrix. | |
Prints a probability plot. | |
Performs iterative proportional fitting of a contingency table using a loglinear model. |
Q
Performs a Cochran Q test for related observations. | |
Forms a k-d tree. |
R
Evaluates the Rayleigh cumulative distribution function. | |
Evaluates the inverse of the Rayleigh cumulative distribution function. | |
Evaluates the Rayleigh probability density function. | |
Computes the ranks, normal scores, or exponential scores for a vector of observations. | |
Computes a robust estimate of a covariance matrix and mean vector. | |
Selects the best multiple linear regression models. | |
Computes case statistics and diagnostics given data
points, coefficient estimates | |
Computes case statistics for a polynomial regression model given the fit based on orthogonal polynomials. | |
Generates an orthogonal central composite design. | |
Fits a multiple linear regression model given the variance-covariance matrix. | |
Computes the estimated variance-covariance matrix of the estimated regression coefficients given the R matrix. | |
Fits a polynomial curve using least squares. | |
Fits a univariate, non seasonal ARIMA time series model with the inclusion of one or more regression variables. | |
Fits an orthogonal polynomial regression model. | |
Fits a multivariate linear regression model via fast Givens transformations. | |
Fits a multivariate general linear model. | |
Computes the matrix of sums of squares and
crossproducts for the multivariate general linear hypothesis HBU =
G given the coefficient estimates | |
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. | |
Performs response control given a fitted simple linear regression model. | |
Performs inverse prediction given a fitted simple linear regression model. | |
Fits a multiple linear regression model using the least absolute values criterion. | |
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. | |
Fits a line to a set of data points using least squares. | |
Fits a multiple linear regression model using the Lp norm criterion. | |
Fits a multiple linear regression model using the minimax criterion. | |
Computes a lack-of-fit test based on exact replicates for a fitted regression model. | |
Computes a lack-of-fit test based on near replicates for a fitted regression model. | |
Fits a multiple linear regression model using least squares. | |
Generates a time series from a specified ARMA model. | |
Generates pseudorandom numbers from a beta distribution. | |
Generates pseudorandom numbers from a binomial distribution. | |
Generates pseudorandom numbers from a chi-squared distribution. | |
Generates pseudorandom numbers from a Cauchy distribution. | |
Generates a pseudorandom orthogonal matrix or a correlation matrix. | |
Generates pseudorandom numbers from a multivariate distribution determined from a given sample. | |
Generates pseudorandom numbers from a standard exponential distribution. | |
Generates pseudorandom numbers from a mixture of two exponential distributions. | |
Generates pseudorandom numbers from an extreme value distribution. | |
Generates pseudorandom numbers from the F distribution. | |
Generates pseudorandom numbers from a standard gamma distribution. | |
Sets up table to generate pseudorandom numbers from a general continuous distribution. | |
Generates pseudorandom numbers from a general continuous distribution. | |
Generates pseudorandom numbers from a general discrete distribution using an alias method. | |
Sets up table to generate pseudorandom numbers from a general discrete distribution. | |
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. |
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. |
Generates pseudorandom numbers from a hypergeometric distribution. | |
Initializes the 32-bit Merseene Twister generator using an array. | |
Retrieves the current table used in the 32-bit Mersenne Twister generator. | |
Sets the current table used in the 32-bit Mersenne Twister generator. | |
Initializes the 32-bit Merseene Twister generator using an array. | |
Retrieves the current table used in the 64-bit Mersenne Twister generator | |
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). |
Performs the Wilcoxon rank sum test. | |
Generates pseudorandom numbers from a logarithmic distribution. | |
Fits a nonlinear regression model. | |
Generates pseudorandom numbers from a lognormal distribution. | |
Generates pseudorandom numbers from a multinomial distribution. | |
Generates pseudorandom numbers from a multivariate Gaussian Copula distribution. | |
Generates a length N output vector R of pseudorandom numbers from a Student’s t Copula distribution. | |
Generates pseudorandom numbers from a multivariate normal distribution. | |
Generates pseudorandom numbers from a negative binomial distribution. | |
Generates pseudorandom numbers from a standard normal distribution using an acceptance/rejection method. | |
Generates a pseudorandom number from a standard normal distribution. | |
Generates pseudorandom numbers from a standard normal distribution using an inverse CDF method. | |
Generates pseudorandom order statistics from a standard normal distribution. | |
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. |
Generates a pseudorandom permutation. | |
Generates pseudorandom numbers from a Poisson distribution. | |
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. |
Generates pseudorandom points on a unit circle or K-dimensional sphere. | |
Generates a simple pseudorandom sample of indices. | |
Generates a simple pseudorandom sample from a finite population. | |
Generates pseudorandom numbers from a stable distribution. | |
Generates pseudorandom numbers from a Student’s t distribution. | |
Generates a pseudorandom two-way table. | |
Generates pseudorandom numbers from a triangular distribution on the interval (0,1). | |
Generates pseudorandom numbers from a uniform (0,1) distribution. | |
Generates pseudorandom numbers from a discrete uniform distribution. | |
Generates a pseudorandom number from a uniform (0, 1) distribution. | |
Generates pseudorandom order statistics from a uniform (0, 1) distribution. | |
Generates pseudorandom numbers from a von Mises distribution. | |
Generates pseudorandom numbers from a Weibull distribution. | |
Analyzes a simple linear regression model. | |
Reorders rows and columns of a symmetric matrix. | |
Reorders the responses from a balanced complete experimental design. | |
Computes diagnostics for detection of outliers and influential data points given residuals and the R matrix for a fitted general linear model. | |
Analyzes a polynomial regression model. | |
Computes summary statistics for a polynomial regression model given the fit based on orthogonal polynomials. | |
Computes statistics related to a regression fit given
the coefficient estimates | |
Builds multiple linear regression models using forward selection, backward selection, or stepwise selection. | |
Retrieves a symmetric submatrix from a symmetric matrix. | |
Performs a runs up test. |
S
Computes simultaneous confidence intervals on all pairwise differences of means. | |
Sorts columns of a real rectangular matrix using keys in rows. | |
Prints a scatterplot of several groups of data. | |
Performs the Cox and Stuart sign test for trends in dispersion and location. | |
Determines an optimal differencing for seasonal adjustments of a time series. | |
Performs a sign test of the hypothesis that a given value is a specified quantile of a distribution. | |
Computes statistics for inferences regarding the population proportion and total, given proportion data from a simple random sample. | |
Computes statistics for inferences regarding the population proportion and total, given proportion data from a stratified random sample. | |
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. | |
Computes statistics for inferences regarding the population mean and total using ratio or regression estimation, given continuous data from a stratified random sample. | |
Computes statistics for inferences regarding the population mean and total using single-stage cluster sampling with continuous data. | |
Computes statistics for inferences regarding the population mean and total, given data from a simple random sample. | |
Computes statistics for inferences regarding the population mean and total, given data from a stratified random sample. | |
Computes statistics for inferences regarding the population mean and total, given continuous data from a two-stage sample with equisized primary units. | |
Performs Student-Newman-Keuls multiple comparison test. | |
Performs a Wilcoxon signed rank test. | |
Computes the Wiener forecast operator for a stationary stochastic process. | |
Performs a Shapiro-Wilk W-test for normality. | |
Searches a sorted vector for a given scalar and return its index. | |
Sorts rows of a real rectangular matrix using keys in columns. | |
Searches a character vector, sorted in ascending ASCII order, for a given string and return its index. | |
Estimates the nonnormalized spectral density of a stationary time series using a spectral window given the time series data. | |
Estimates the nonnormalized spectral density of a stationary time series using a spectral window given the periodogram. | |
Estimates survival probabilities and hazard rates for various parametric models. | |
Prints a stem-and-leaf plot. | |
Analyzes censored survival data using a generalized linear model. | |
Sorts an integer array by algebraic value. | |
Sorts an integer array by algebraic value and returns the permutations. | |
Sorts a real array by algebraic value. | |
Sorts a real array by algebraic value and returns the permutations. | |
Estimation of the nonnormalized spectral density of a stationary time series based on specified periodogram weights given the time series data. | |
Estimation of the nonnormalized spectral density of a stationary time series based on specified periodogram weights given the periodogram. |
T
Transforms coefficients from a quadratic regression model generated from squares and crossproducts of centered variables to a model using uncentered variables. | |
Gets today’s date. | |
Evaluates the Student’s t cumulative distribution function. | |
Categorizes bivariate data and compute the tetrachoric correlation coefficient. | |
Computes preliminary estimates of parameters for a univariate transfer function model. | |
Gets time of day. | |
Evaluates the inverse of the Student’s t distribution function. | |
Evaluates the noncentral Student’s t cumulative distribution function. | |
Evaluates the inverse of the noncentral Student’s t cumulative distribution function. | |
This function evaluates the noncentral Student's t probability density function. | |
This function evaluates the Student’s t probability density function. | |
Prints a binary tree. | |
Computes Turnbull’s generalized Kaplan-Meier estimates of survival probabilities in samples with interval censoring. | |
Detects and determines outliers and simultaneously estimates the model parameters in a time series. | |
Computes forecasts for an outlier contaminated | |
Tallies observations into a two-way frequency table. | |
Computes statistics for mean and variance inferences using samples from two normal populations. |
U
Sets or retrieves input or output device unit numbers. | |
Evaluates the discrete uniform cumulative distribution function. | |
Evaluates the uniform cumulative distribution function. | |
Evaluates the inverse of the discrete uniform cumulative distribution function. | |
Evaluates the discrete uniform probability density function. | |
Evaluates the inverse of the uniform cumulative distribution function. | |
Evaluates the uniform probability density function. | |
Computes basic univariate statistics. |
V
Obtains STAT/LIBRARY-related version, system and license numbers. | |
Prints a vertical histogram with every bar subdivided into two parts. | |
Prints a vertical histogram. |
W
Evaluates the Weibull cumulative distribution function. | |
Evaluates the inverse of the Weibull cumulative distribution function. | |
Evaluates the Weibull probability density function. | |
Prints an integer rectangular matrix with a given format and labels. | |
Prints an integer rectangular matrix with integer row and column labels. | |
Sets or retrieves an option for printing a matrix. | |
Prints a real rectangular matrix with a given format and labels. | |
Prints a real rectangular matrix with integer row and column labels. |
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