Package: pcdreg 0.1.0.9000

pcdreg: Semiparametric Regression for Panel Count Data

Fits semiparametric regression models to panel count data, that is, to recurrent event processes observed only at intermittent examination times, allowing covariates to be time-varying. The proportional rate model is fitted by nonparametric maximum likelihood through an expectation-maximization algorithm under a working Poisson assumption, while inference does not require that assumption to hold; two computationally inexpensive covariance estimators are provided, a robust sandwich estimator that remains consistent when the Poisson assumption fails and an efficient information estimator that is a fast substitute for profile likelihood when it holds, with the profile likelihood estimator available for comparison. The proportional means model is fitted by the estimating equation of Hu, Sun and Wei (2003) <doi:10.1111/1467-9469.00316>. Methods for the rate model are described in Sun, Guo, Li, Tu and Sun (2024) <doi:10.3150/23-BEJ1713>.

Authors:Dayu Sun [aut, cre], Yuanyuan Guo [ctb], Yang Li [ctb], Wanzhu Tu [ctb], Jianguo Sun [ctb]

pcdreg_0.1.0.9000.tar.gz
pcdreg_0.1.0.9000.zip(r-4.7-x86_64)pcdreg_0.1.0.9000.zip(r-4.7-arm64)pcdreg_0.1.0.9000.zip(r-4.6-x86_64)pcdreg_0.1.0.9000.zip(r-4.6-arm64)pcdreg_0.1.0.9000.zip(r-4.5-x86_64)
pcdreg_0.1.0.9000.tgz(r-4.6-x86_64)pcdreg_0.1.0.9000.tgz(r-4.6-arm64)pcdreg_0.1.0.9000.tgz(r-4.5-x86_64)pcdreg_0.1.0.9000.tgz(r-4.5-arm64)
pcdreg_0.1.0.9000.tar.gz(r-4.7-arm64)pcdreg_0.1.0.9000.tar.gz(r-4.7-x86_64)pcdreg_0.1.0.9000.tar.gz(r-4.6-arm64)pcdreg_0.1.0.9000.tar.gz(r-4.6-x86_64)
pcdreg_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pcdreg/json (API)

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

Bug tracker:https://github.com/dayusun/pcdreg/issues

Pkgdown/docs site:https://www.sundayu.me

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

em-algorithmpanel-count-datarecurrent-eventssemiparametric-modelsstatisticssurvival-analysisopenblascpp

4.77 score 13 scripts 10 exports 25 dependencies

Last updated from:d0dc0f492a. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK140
linux-devel-x86_64OK161
source / vignettesOK243
linux-release-arm64OK134
linux-release-x86_64OK163
macos-release-arm64OK187
macos-release-x86_64OK493
macos-oldrel-arm64OK286
macos-oldrel-x86_64OK574
windows-devel-arm64OK415
windows-devel-x86_64OK267
windows-release-arm64OK405
windows-release-x86_64OK262
windows-oldrel-x86_64OK282
wasm-releaseOK119

Exports:augmentautoplotbaselineglanceis.pcdpcdpcdregpcdreg_controlsim_pcdtidy

Dependencies:clicpp11farvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangS7scalestibbleutf8vctrsviridisLitewithr

Where pcdreg fits among R packages for panel count data
The landscape | What is actually different | The rate model | Monotonicity | Covariates between examinations | Variance without the Poisson assumption | Seeing the data first | How long they take | Which standard errors are available | Timings | Why the rate model flattens | Caveats | Which to use | Reference
Rendered fromcomparison.Rmdusingknitr::rmarkdown

Last update: 2026-08-13
Started: 2026-08-12

Preparing data and specifying the model
What panel count data is | The response | No time-varying covariates: one row per examination | Time-varying covariates: counting process rows | Which form, in one line | The formula | There is no intercept | Row order does not matter | Recipes for common layouts | Cumulative counts | Data held in the older panelEM layout | Examinations and covariate histories in separate tables | One row per subject, several visit columns | What the data must satisfy | What gets dropped | Missing values | What the package does with your rows | Checking before you fit | See also
Rendered fromdata-preparation.Rmdusingknitr::rmarkdown

Last update: 2026-08-12
Started: 2026-08-12

Regression analysis of panel count data
The data and the model | Getting data into shape | Converting data held in the older layout | Fitting | Which standard errors | What happens when the Poisson assumption fails | The baseline and predicted means | Tidy output | The means model | Computation | References
Rendered frompcdreg.Rmdusingknitr::rmarkdown

Last update: 2026-08-12
Started: 2026-08-12