ArvindRF: Random Forest Regression with Arvind Distribution Error Model

Implements Random Forest regression under the Arvind distribution error model. Provides core distribution functions (density, cumulative distribution, quantile, random generation, hazard, survival), parameter estimation via Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC), non-parametric bootstrap confidence intervals (at 90%, 95%, and 99% levels), Highest Posterior Density (HPD) intervals, model evaluation metrics (estimated values, bias, mean squared error, risk value), homoscedastic prediction intervals, and goodness-of-fit diagnostic tests (Kolmogorov-Smirnov and Anderson-Darling tests, Akaike Information Criterion, and Bayesian Information Criterion). References: Breiman (2001) <doi:10.1023/A:1010933404324>; Wright and Ziegler (2017) <doi:10.18637/jss.v077.i01>.

Version:	1.0.0
Depends:	R (≥ 4.0.0)
Imports:	ranger, coda, goftest, stats, graphics
Suggests:	testthat (≥ 3.0.0)
Published:	2026-08-21
DOI:	10.32614/CRAN.package.ArvindRF (may not be active yet)
Author:	Shikhar Tyagi [aut, cre], Aruna Rajballie [aut], Vrijesh Tripathi [aut]
Maintainer:	Shikhar Tyagi <shikhar1093tyagi at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
Language:	en-US
CRAN checks:	ArvindRF results

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