pprof: Modeling, Standardization and Testing for Provider Profiling
Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.
| Version: | 1.0.2 |
| Depends: | R (≥ 4.1.0) |
| Imports: | Rcpp,RcppParallel, stats,caret,olsrr,pROC,poibin,dplyr,ggplot2,Matrix,lme4,magrittr,scales,tibble,rlang |
| LinkingTo: | Rcpp,RcppArmadillo,RcppParallel |
| Suggests: | knitr,rmarkdown,testthat (≥ 3.0.0) |
| Published: | 2025-06-20 |
| DOI: | 10.32614/CRAN.package.pprof |
| Author: | Xiaohan Liu [aut, cre], Lingfeng Luo [aut], Yubo Shao [aut], Xiangeng Fang [aut], Wenbo Wu [aut], Kevin He [aut] |
| Maintainer: | Xiaohan Liu <xhliuu at umich.edu> |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/UM-KevinHe/pprof |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| Materials: | README |
| CRAN checks: | pprof results |
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