Generates pseudorandom ARMA process numbers.

Generates pseudorandom numbers from a beta distribution.

Generates pseudorandom binomial numbers.

Generates pseudorandom numbers from a Cauchy distribution.

Generates pseudorandom numbers from a chi-squared distribution.

Generates pseudorandom numbers from a standard exponential distribution.

Generates pseudorandom mixed numbers from a standard exponential distribution.

Generates pseudorandom numbers from a standard gamma distribution.

Generates pseudorandom numbers from a general continuous distribution.

Generates pseudorandom numbers from a general discrete distribution using an alias method or optionally a table lookup method.

Generates pseudorandom numbers from a geometric distribution.

Retrieves the current table used in the GFSR generator.

Sets the current table used in the GFSR generator.

Generates pseudorandom numbers from a hypergeometric distribution.

Generates pseudorandom numbers from a logarithmic distribution.

Generates pseudorandom numbers from a lognormal 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 64-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.

Generates pseudorandom numbers from a multinomial distribution.

Generates pseudorandom numbers from a multivariate distribution determined from a given sample.

Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Gaussian Copula distribution.

Given a Cholesky factorization of a correlation matrix, generates pseudorandom numbers from a Student’s t Copula distribution.

Generates pseudorandom numbers from a negative binomial distribution.

Generates pseudorandom numbers from a normal, N (μ, σ2), distribution.

Generates pseudorandom numbers from a multivariate normal distribution.

Generates pseudorandom numbers from a nonhomogeneous Poisson process.

Selects the uniform (0, 1) multiplicative congruential pseudorandom number generator.

Retrieves the uniform (0, 1) multiplicative congruential pseudorandom number generator.

Generates pseudorandom order statistics from a standard normal distribution.

Generates pseudorandom order statistics from a uniform (0, 1) distribution.

Generates a pseudorandom orthogonal matrix or a correlation matrix.

Generates a pseudorandom permutation.

Generates pseudorandom numbers from a Poisson distribution.

Generates a simple pseudorandom sample from a finite population.

Generates a simple pseudorandom sample of indices.

Retrieves the current value of the seed used in the IMSL random number generators.

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

Generates pseudorandom points on a unit circle or K-dimensional sphere.

Generates pseudorandom numbers from a stable distribution.

Generates pseudorandom Student's t.

Retrieves a seed for the congruential generators that do not do shuffling that will generate random numbers beginning 100,000 numbers farther along.

Retrieves the current table used in the shuffled generator.

Sets the current table used in the shuffled generator.

Generates a pseudorandom two-way table.

Generates pseudorandom numbers from a triangular distribution.

Generates pseudorandom numbers from a uniform (0, 1) distribution.

Generates pseudorandom numbers from a discrete uniform distribution.

Generates pseudorandom numbers from a von Mises distribution.

Generates pseudorandom numbers from a Weibull distribution.

Performs a test for randomness.

Computes the ranks, normal scores, or exponential scores for a vector of observations.

Analyzes data from balanced and unbalanced randomized complete-block experiments.

Fits a multiple linear regression model using least squares.

Fits a univariate, non-seasonal ARIMA time series model with the inclusion of one or more regression variables.

Computes predicted values, confidence intervals, and diagnostics after fitting a regression model.

Selects the best multiple linear regression models.

Builds multiple linear regression models using forward selection, backward selection or stepwise selection.

Produces summary statistics for a regression model given the information from the fit.

Generates regressors for a general linear model.

Computes a robust estimate of a covariance matrix and mean vector.