DPQ-package: Density, Probability, Quantile ('DPQ') Computations

In DPQ: Density, Probability, Quantile ('DPQ') Computations

Density, Probability, Quantile ('DPQ') Computations

Description

Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. – For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) <doi:10.1002/bimj.4710360207> and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.

Details

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An important goal is to investigate diverse algorithms and approximations of R's own density (d*()), probability (p*()), and quantile (q*()) functions, notably in “border” cases where the traditional published algorithms have shown to be suboptimal, not quite accurate, or even useless.

Examples are border cases of the beta distribution, or non-central distributions such as the non-central chi-squared and t-distributions.

Author(s)

Principal author and maintainer: Martin Maechler <[e-mail]>

See Also

The package DPQmpfr, which builds on this package and on Rmpfr.

Examples

## Show problem in R's non-central t-distrib. density (and approximations):
example(dntJKBf)

DPQ documentation built on Dec. 5, 2023, 3:05 a.m.