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 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.

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.

Retrieves the current value of the array used in the IMSL GFSR random number generator.

Generates pseudorandom numbers from a geometric distribution.

Retrieves the current value of the table in the IMSL random number generators that use shuffling.

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 Mersenne 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 Mersenne 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.

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.

Retrieves the indicator of the type of uniform random number generator.

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.

Initializes the array used in the IMSL GFSR random number generator.

Initializes the table in the IMSL random number generators that use shuffling.

Initializes a random seed for use in the IMSL random number 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.

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.