| Type | Location | Extends | Description |
|---|---|---|---|
| anova_factor | fstats_anova | None | Defines an ANOVA factor result. |
| array_container | fstats_types | None | Provides a container for a real-valued array. A practical use of this construct is in the construction of jagged arrays. |
| binomial_distribution | fstats_distributions | distribution | Defines a binomial distribution. The binomial distribution describes the probability p of getting k successes in n independent trials. |
| bootstrap_statistics | fstats_bootstrap | None | A collection of statistics resulting from the bootstrap process. |
| chain_builder | fstats_mcmc | None | A type allowing for the construction of chain of values. |
| chi_squared_distribution | fstats_distributions | distribution | Defines a Chi-squared distribution. |
| convergence_info | fstats_regression | None | Provides information regarding convergence status. |
| distribution | fstats_distributions | None | Defines a probability distribution. |
| doe_model | fstats_experimental_design | None | A model used to represent a design of experiments result. The model is of the following form. |
| f_distribution | fstats_distributions | distribution | Defines an F-distribution. |
| iteration_controls | fstats_regression | None | Provides a collection of iteration control parameters. |
| lm_solver_options | fstats_regression | None | Options to control the Levenberg-Marquardt solver. |
| log_normal_distribution | fstats_distributions | distribution | Defines a normal distribution. |
| mcmc_proposal | fstats_mcmc | None | Defines a type responsible for generating a proposal state for a Monte-Carlo, Markov-Chain sampler. |
| mcmc_sampler | fstats_mcmc | chain_builder | An implementation of the Metropolis-Hastings algorithm for the generation of a Markov chain. |
| mcmc_target | fstats_mcmc | None | Defines a model of the target distribution(s). This type is key to the MCMC regression process. The approach taken is to evaluate the model provided here and evaluating its likelihood. The likelihood is evaluated by computing the residual between the model and data, and making the assumption that the residual should be normally distributed. |
| multivariate_distribution | fstats_distributions | None | Defines a multivariate probability distribution. |
| multivariate_normal_distribution | fstats_distributions | multivariate_distribution | Defines a multivariate normal (Gaussian) distribution. |
| normal_distribution | fstats_distributions | distribution | Defines a normal distribution. |
| regression_statistics | fstats_regression | None | A container for regression-related statistical information. |
| single_factor_anova_table | fstats_anova | None | Defines a single-factor ANOVA results table. |
| t_distribution | fstats_distributions | distribution | Defines Student's T-Distribution. |
| two_factor_anova_table | fstats_anova | None | Defines a two-factor ANOVA results table. |