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 + file LICENSE |
| URL: |
https://github.com/UM-KevinHe/pprof |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make |
| Materials: |
README |
| CRAN checks: |
pprof results |
Documentation:
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