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  "Title": "Random Forest Regression with Arvind Distribution Error Model",
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  "Description": "Implements Random Forest regression under the Arvind\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, model evaluation metrics (estimated values, bias,\nmean squared error, risk value), homoscedastic prediction\nintervals, and goodness-of-fit diagnostic tests\n(Kolmogorov-Smirnov and Anderson-Darling tests, Akaike\nInformation Criterion, and Bayesian Information Criterion).\nReferences: Breiman (2001) <doi:10.1023/A:1010933404324>;\nWright and Ziegler (2017) <doi:10.18637/jss.v077.i01>.",
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        "parvind",
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