Package: pprof 1.0.1

Xiaohan Liu

pprof: Risk-adjusted Models, Standardized Measure Calculation, Hypothesis Testing and Visualization 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]

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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'))

Peer review:

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:

openblascppopenmp

3.98 score 3 scripts 8 exports 124 dependencies

Last updated 13 days agofrom:7b0f6a742e. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 13 2024
R-4.5-win-x86_64NOTEDec 13 2024
R-4.5-linux-x86_64NOTEDec 13 2024
R-4.4-win-x86_64NOTEDec 13 2024
R-4.4-mac-x86_64NOTEDec 13 2024
R-4.4-mac-aarch64NOTEDec 13 2024
R-4.3-win-x86_64NOTEDec 13 2024
R-4.3-mac-x86_64NOTEDec 13 2024
R-4.3-mac-aarch64NOTEDec 13 2024

Exports:bar_plotcaterpillar_plotdata_checklinear_felinear_relogis_feSM_outputtest

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacaretclasscliclockcodetoolscolorspacecommonmarkcowplotcpp11crayondata.tableDerivdiagramdigestdoBydplyre1071fansifarverfastmapfontawesomeforeachFormulafsfuturefuture.applygenericsggplot2globalsgluegoftestgowergridExtragtablehardhathtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothlabelinglaterlatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqaModelMetricsmodelrmunsellnlmenloptrnnetnortestnumDerivolsrrparallellypbkrtestpillarpkgconfigplyrpoibinpROCprodlimprogressrpromisesproxypurrrquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrecipesreshape2rlangrpartsassscalesshapeshinysourcetoolsSparseMSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxplorerrxtable

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
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
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