MTLRF: Random Forest Regression with Modified Topp-Leone Error Model
Implements Random Forest regression under the Modified Topp-Leone (MTL) distribution error model. Provides core distribution functions (density, cumulative distribution, exact closed-form quantile, random generation, hazard, and survival), parameter estimation via closed-form Expectation-Maximization/Maximum Likelihood (EM/MLE) and Bayesian Markov Chain Monte Carlo (MCMC), non-parametric bootstrap confidence intervals (at 90%, 95%, and 99% levels), Highest Posterior Density (HPD) intervals, Heidelberger and Welch MCMC convergence diagnostics, 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>; Singh, Tyagi, Singh, and Tyagi (2025) <https://statassoc.or.th>; Topp and Leone (1955) <doi:10.1080/01621459.1955.10501259>; Wright and Ziegler (2017) <doi:10.18637/jss.v077.i01>; Plummer, Best, Cowles, and Vines (2006) <https://CRAN.R-project.org/package=coda>; Heidelberger and Welch (1983) <doi:10.1287/opre.31.6.1109>.
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[aut, cre],
Aruna Rajballie [aut],
Vrijesh Tripathi [aut]