Orphaned and corrected on 2019-07-02 as installation
issues were not addressed.

LTO, mismatching C unsigned int to INTEGER.

c(person("Houtao", "Deng", role = c("aut", "cre"),
email = "softwaredeng@gmail.com"),
person("Xin", "Guan", role = "aut"),
person("Andy", "Liaw", role = "aut"),
person("Leo", "Breiman", role = "aut"),
person("Adele", "Cutler", role = "aut"))
Feature Selection with Regularized Random Forest. This
package is based on the 'randomForest' package by Andy Liaw.
The key difference is the RRF() function that builds a
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>.
Houtao Deng [aut, cre],
Xin Guan [aut],
Andy Liaw [aut],
Leo Breiman [aut],
Adele Cutler [aut]

Residual Randomization Inference for Regression Models
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.

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.

Reduced-Rank Multinomial Logistic Regression for Markov Chains
c(person("Pei", "Wang", role = c("aut", "cre"),
email = "wangp33@miamioh.edu"),
person("Richard", "Kryscio", role = "aut") )
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
two bootstrap methods.
Pei Wang [aut, cre],
Richard Kryscio [aut]