PowerXgammaRF: Random Forest Regression with Power Xgamma Distribution Error
Model
Implements Random Forest regression under the Power Xgamma 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, Heidelberger and Welch's MCMC convergence diagnostic, convergence probability, 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: Tyagi et al. (2022, Int. J. Stat. Reliab. Eng., 9(1), 51-60); Breiman (2001) <doi:10.1023/A:1010933404324>; Wright and Ziegler (2017) <doi:10.18637/jss.v077.i01>; Heidelberger and Welch (1983) <doi:10.1287/opre.31.6.1109>; Sen et al. (2016) <doi:10.22237/jmasm/1462076400>.
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[aut, cre],
Aruna Rajballie [aut],
Vrijesh Tripathi [aut]