rpms: Recursive Partitioning for Modeling Survey Data

Functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types.

Version: 0.5.1
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.3), stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: parallel
Published: 2021-06-25
DOI: 10.32614/CRAN.package.rpms
Author: Daniell Toth [aut, cre]
Maintainer: Daniell Toth <danielltoth at yahoo.com>
License: CC0
NeedsCompilation: yes
In views: OfficialStatistics
CRAN checks: rpms results


Reference manual: rpms.pdf


Package source: rpms_0.5.1.tar.gz
Windows binaries: r-devel: rpms_0.5.1.zip, r-release: rpms_0.5.1.zip, r-oldrel: rpms_0.5.1.zip
macOS binaries: r-release (arm64): rpms_0.5.1.tgz, r-oldrel (arm64): rpms_0.5.1.tgz, r-release (x86_64): rpms_0.5.1.tgz, r-oldrel (x86_64): rpms_0.5.1.tgz
Old sources: rpms archive

Reverse dependencies:

Reverse imports: mase
Reverse suggests: surveyCV


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