Package: pprof 1.0.3

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_1.0.3.tar.gz
pprof_1.0.3.zip(r-4.7)pprof_1.0.3.zip(r-4.6)pprof_1.0.3.zip(r-4.5)
pprof_1.0.3.tgz(r-4.6-x86_64)pprof_1.0.3.tgz(r-4.6-arm64)pprof_1.0.3.tgz(r-4.5-x86_64)pprof_1.0.3.tgz(r-4.5-arm64)
pprof_1.0.3.tar.gz(r-4.7-arm64)pprof_1.0.3.tar.gz(r-4.7-x86_64)pprof_1.0.3.tar.gz(r-4.6-arm64)pprof_1.0.3.tar.gz(r-4.6-x86_64)
pprof_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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:

openblascppopenmp

3.30 score 6 scripts 279 downloads 12 exports 131 dependencies

Last updated from:5260838ad3. Checks:10 OK, 3 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK317
linux-devel-x86_64OK309
source / vignettesOK258
linux-release-arm64ERROR248
linux-release-x86_64ERROR268
macos-release-arm64OK217
macos-release-x86_64OK376
macos-oldrel-arm64OK254
macos-oldrel-x86_64OK400
windows-develERROR271
windows-releaseOK396
windows-oldrelOK394
wasm-releaseOK177

Exports:bar_plotcaterpillar_plotdata_checklinear_crelinear_felinear_relogis_crelogis_felogis_firthlogis_reSM_outputtest

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacaretclasscliclockcodetoolscolorspacecommonmarkcowplotcpp11data.tableDerivdiagramdigestdoBydplyre1071farverfastmapfontawesomeforeachforecastFormulafracdifffsfuturefuture.applygenericsggplot2globalsgluegoftestgowergridExtragtablehardhathtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothlabelinglaterlatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmemoisemgcvmimeminqaModelMetricsmodelrnlmenloptrnnetnortestnumDerivolsrrotelparallellypbkrtestpillarpkgconfigplyrpoibinpROCprodlimprogressrpromisesproxypurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackrecipesreformulasreshape2rlangrpartS7sassscalesshapeshinysourcetoolsSparseMsparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdburcautf8vctrsviridisLitewithrxplorerrxtablezoo

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_cre' objectconfint.linear_cre
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_cre' objectconfint.logis_cre
Get confidence intervals for provider effects or standardized measures from a fitted 'logis_fe' objectconfint.logis_fe
Get confidence intervals for provider effects or standardized measures from a fitted 'logis_re' objectconfint.logis_re
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 correlated random effect linear modellinear_cre
Main function for fitting the fixed effect linear modellinear_fe
Main Function for fitting the random effect linear modellinear_re
Main Function for fitting correlated random effect logistic modellogis_cre
Main function for fitting the fixed effect logistic modellogis_fe
Main function for fitting the fixed effect logistic model using firth correctionlogis_firth
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_cre' objectSM_output.linear_cre
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_cre' objectSM_output.logis_cre
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', 'linear_re' or 'linear_cre' objectsummary.linear_cre summary.linear_fe summary.linear_re
Result Summaries of Covariate Estimates from a fitted 'logis_re' or 'logis_cre' objectsummary.logis_cre summary.logis_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_cre' objecttest.linear_cre
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_cre' objecttest.logis_cre
Conduct hypothesis testing for provider effects from a fitted 'logis_fe' objecttest.logis_fe
Conduct hypothesis testing for provider effects from a fitted 'logis_re' objecttest.logis_re