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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=pprof to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4