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varPro - Model-Independent Variable Selection via the Rule-Based Variable Priority

A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.

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openmp

5.17 score 3 stars 3 dependents 11 scripts 864 downloads

randomForestRHF - Random Hazard Forests

Random Hazard Forests (RHF) extend Random Survival Forests (RSF) by directly estimating the hazard function and by accommodating time-dependent covariates through counting-process style inputs. The package fits tree ensembles for dynamic survival prediction, returning hazard, cumulative hazard, integrated hazard, and related performance summaries for training and test data. The methods build on Random Survival Forests described by Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> and on nonparametric hazard modeling with time-dependent covariates described by Lee et al. (2021) <doi:10.1214/20-AOS2028>.

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openmp

3.00 score 2 stars 577 downloads

randomForestSGT - Random Forest Super Greedy Trees

Super greedy trees (SGTs) with univariate and multivariate geometric cuts are obtained using lasso and coordinate descent.

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openmp

2.70 score 358 downloads

spikeslab - Prediction and Variable Selection Using Spike and Slab Regression

Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.

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2.63 score 1 stars 2 dependents 71 scripts 678 downloads

boostmtree - Boosted Multivariate Trees for Longitudinal Data

Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.

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1.00 score 9 scripts 494 downloads