Andy Liaw and Matthew Wiener, Regularized random forest for classification by
Houtao Deng. Regularized random forest for regression by Xin Guan.
The key difference is the RRF() function that builds a

Packaged: 2018-06-30 06:09:23 UTC; houtaodeng

X-CRAN-Comment: Orphaned and corrected on 2019-07-02 as installation
LTO, mismatching C unsigned int to INTEGER.

Authors@R: c(person("Houtao", "Deng", role = c("aut", "cre"),
person("Adele", "Cutler", role = "aut"))
regularized random forest. Fortran original by Leo Breiman
and Adele Cutler, R port by Andy Liaw and Matthew Wiener,
Regularized random forest for classification by Houtao Deng,
Regularized random forest for regression by Xin Guan.
Reference: Houtao Deng (2013) <arXiv:1306.0237>.
Packaged: 2022-02-24 04:04:21 UTC; houtaodeng

Packaged: 2022-05-30 18:55:20 UTC; houtaodeng

Packaged: 2018-07-17 05:00:47 UTC; houtaodeng

Title: Residual Randomization Inference for Regression Models
Description: Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors. This includes cluster errors, and exchangeability within clusters or sign symmetry across clusters.

Description: Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.

Title: Reduced-Rank Multinomial Logistic Regression for Markov Chains
Authors@R: c(person("Pei", "Wang", role = c("aut", "cre"),
person("Richard", "Kryscio", role = "aut") )
Description: Fit the reduced-rank multinomial logistic regression model for Markov
chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio
(2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial
logistic regression in Markov chains and reduced-rank. It is very useful in
a study where multi-states model is assumed and each transition among the
states is controlled by a series of covariates. The key advantage is to
reduce the number of parameters to be estimated. The final coefficients for
all the covariates and the p-values for the interested covariates will be
reported. The p-values for the whole coefficient matrix can be calculated by