Derived Types

Defines an ANOVA factor result.

Provides a container for a real-valued array. A practical use of this construct is in the construction of jagged arrays.

Defines a binomial distribution. The binomial distribution describes the probability p of getting k successes in n independent trials.

A collection of statistics resulting from the bootstrap process.

Defines a Chi-squared distribution.

Provides information regarding convergence status.

Defines a probability distribution.

A model used to represent a design of experiments result. The model is of the following form.

Defines an F-distribution.

Provides a collection of iteration control parameters.

Options to control the Levenberg-Marquardt solver.

Defines a normal distribution.

The mcmc_regression type extends the metropolis_hastings type to specifically target regression problems. The problem is formulated such that the target distribution takes the form $y \sim N \left( f(x), \sigma^{2} \right)$, where $N$ is a normal distribution with $f(x)$ as the mean and the model variance, $\sigma^2$ is determined by computing the variance for the current estimate of the model.

An implementation of the Metropolis-Hastings algorithm for the generation of a Markov chain. This is a default implementation that allows sampling of normally distributed posterior distributions centered on zero with unit standard deviations. Proposals are generated from a multivariate normal distribution with an identity covariance matrix and centered on zero. To alter these sampling and target distributions simply create a new class inheriting from this class and override the appropriate routines.

Defines a multivariate probability distribution.

Defines a multivariate normal (Gaussian) distribution.

Defines a normal distribution.

A container for regression-related statistical information.

Defines a single-factor ANOVA results table.

Defines Student's T-Distribution.

Defines a two-factor ANOVA results table.