Package: rfvimptest 0.1.4

rfvimptest: Sequential Permutation Testing of Random Forest Variable Importance Measures

Sequential permutation testing for statistical significance of predictors in random forests. The main function of the package is rfvimptest(), which allows to test for the statistical significance of predictors in random forests using different (sequential) permutation test strategies [1]. The advantage of sequential over conventional permutation tests is that they are computationally considerably less intensive, as the sequential procedure is stopped as soon as there is sufficient evidence for either the null or the alternative hypothesis. Reference: [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation testing of variable importance measures by the example of random forests. Computational Statistics & Data Analysis 181:107689, <doi:10.1016/j.csda.2022.107689>.

Authors:Alexander Hapfelmeier [aut], Roman Hornung [aut, cre]

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rfvimptest/json (API)

Peer review:

Bug tracker:https://github.com/romanhornung/rfvimptest/issues

On CRAN:

2 exports 0.74 score 41 dependencies 2 scripts 231 downloads

Last updated 1 years agofrom:40a5c4ba2b. Checks:OK: 7. Indexed: yes.

Exports:allinonerfvimptest

Dependencies:classclicodetoolscoindata.tablediagramdigestfuturefuture.applyglobalsipredKernSmoothlatticelavalibcoinlistenvMASSMatrixmatrixStatsmodeltoolsmultcompmvtnormnnetnumDerivparallellypartypermimpprodlimprogressrrandomForestrangerRcppRcppEigenrpartsandwichshapeSQUAREMstrucchangesurvivalTH.datazoo