{ "_id": "6ab0fc873d719c1f8408568c", "Package": "PowerXgammaRF", "Type": "Package", "Title": "Random Forest Regression with Power Xgamma Distribution Error\nModel", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Shikhar\", \"Tyagi\", email = \"shikhar1093tyagi@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1606-0844\")),\nperson(\"Aruna\", \"Rajballie\", email = \"aruna.rajballie@utt.edu.tt\", role = \"aut\"),\nperson(\"Vrijesh\", \"Tripathi\", email = \"vrijesh.tripathi@uwi.edu\", role = \"aut\")\n)", "Description": "Implements Random Forest regression under the Power Xgamma\ndistribution error model. Provides core distribution functions\n(density, cumulative distribution, quantile, random generation,\nhazard, survival), parameter estimation via\nExpectation-Maximization (EM) and Markov Chain Monte Carlo\n(MCMC), non-parametric bootstrap confidence intervals (at 90%,\n95%, and 99% levels), Highest Posterior Density (HPD)\nintervals, Heidelberger and Welch's MCMC convergence\ndiagnostic, convergence probability, model evaluation metrics\n(estimated values, bias, mean squared error, risk value),\nhomoscedastic prediction intervals, and goodness-of-fit\ndiagnostic tests (Kolmogorov-Smirnov and Anderson-Darling\ntests, Akaike Information Criterion, and Bayesian Information\nCriterion). References: Tyagi et al. (2022, Int. J. Stat.\nReliab. Eng., 9(1), 51-60); Breiman (2001)\n; Wright and Ziegler (2017)\n; Heidelberger and Welch (1983)\n; Sen et al. 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