pprof: Modeling, Standardization and Testing for Provider Profiling (original) (raw)
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: | 1.0.1 |
|---|---|
| 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: | 2024-12-12 |
| 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 |
| License: | MIT + file |
| URL: | https://github.com/UM-KevinHe/pprof |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| Materials: | README |
| CRAN checks: | pprof results |
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