Package: pprof 1.0.2

Xiaohan Liu

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>.

Authors:Xiaohan Liu [aut, cre], Lingfeng Luo [aut], Yubo Shao [aut], Xiangeng Fang [aut], Wenbo Wu [aut], Kevin He [aut]

pprof_1.0.2.tar.gz
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pprof.pdf |pprof.html✨
pprof/json (API)

# Install 'pprof' in R:
install.packages('pprof', repos = c('https://um-kevinhe.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/um-kevinhe/pprof/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda-Forge:

openblascppopenmp

4.06 score 3 scripts 194 downloads 9 exports 128 dependencies

Last updated 20 days agofrom:36edd5eb2b. Checks:5 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 14 2025
R-4.5-win-x86_64OKFeb 14 2025
R-4.5-mac-x86_64OKFeb 14 2025
R-4.5-mac-aarch64OKFeb 14 2025
R-4.5-linux-x86_64OKFeb 14 2025
R-4.4-win-x86_64NOTEFeb 14 2025
R-4.4-mac-x86_64NOTEFeb 14 2025
R-4.4-mac-aarch64NOTEFeb 14 2025
R-4.3-win-x86_64NOTEFeb 14 2025
R-4.3-mac-x86_64NOTEFeb 14 2025
R-4.3-mac-aarch64NOTEFeb 14 2025

Exports:bar_plotcaterpillar_plotdata_checklinear_felinear_relogis_felogis_reSM_outputtest

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacaretclasscliclockcodetoolscolorspacecommonmarkcowplotcpp11crayondata.tableDerivdiagramdigestdoBydplyre1071fansifarverfastmapfontawesomeforeachFormulafsfuturefuture.applygenericsggplot2globalsgluegoftestgowergridExtragtablehardhathtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothlabelinglaterlatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqaModelMetricsmodelrmunsellnlmenloptrnnetnortestnumDerivolsrrparallellypbkrtestpillarpkgconfigplyrpoibinpROCprodlimprogressrpromisesproxypurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackrecipesreformulasreshape2rlangrpartsassscalesshapeshinysourcetoolsSparseMsparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxplorerrxtable

Readme and manuals

Help Manual

Help pageTopics
Get a bar plot for flagging percentage overall and stratified by provider sizesbar_plot
Get a caterpillar plot to display confidence intervals for standardized measurescaterpillar_plot
Get confidence intervals for provider effects or standardized measures from a fitted 'linear_fe' objectconfint.linear_fe
Get confidence intervals for provider effects or standardized measures from a fitted 'linear_re' objectconfint.linear_re
Get confidence intervals for provider effects or standardized measures from a fitted 'logis_fe' objectconfint.logis_fe
Data quality check functiondata_check
Early Childhood Longitudinal Study Datasetecls_data
Example data with binary outcomesExampleDataBinary
Example data with continuous outcomesExampleDataLinear
Main function for fitting the fixed effect linear modellinear_fe
Main Function for fitting the random effect linear modellinear_re
Main function for fitting the fixed effect logistic modellogis_fe
Main Function for fitting the random effect logistic modellogis_re
Get funnel plot from a fitted 'linear_fe' object for institutional comparisonsplot.linear_fe
Get funnel plot from a fitted 'logis_fe' object for institutional comparisonsplot.logis_fe
Generic function for calculating standardized measuresSM_output
Calculate direct/indirect standardized differences from a fitted 'linear_fe' objectSM_output.linear_fe
Calculate direct/indirect standardized differences from a fitted 'linear_re' objectSM_output.linear_re
Calculate direct/indirect standardized ratios/rates from a fitted 'logis_fe' objectSM_output.logis_fe
Calculate direct/indirect standardized ratios/rates from a fitted 'logis_re' objectSM_output.logis_re
Result Summaries of Covariate Estimates from a fitted 'linear_fe' or 'linear_re' objectsummary.linear_fe summary.linear_re
Result Summaries of Covariate Estimates from a fitted 'logis_fe' objectsummary.logis_fe
Generic function for hypothesis testing of provider effectstest
Conduct hypothesis testing for provider effects from a fitted 'linear_fe' objecttest.linear_fe
Conduct hypothesis testing for provider effects from a fitted 'linear_re' objecttest.linear_re
Conduct hypothesis testing for provider effects from a fitted 'logis_fe' objecttest.logis_fe