Package: cifmodeling 1.0.0

Shiro Tanaka

cifmodeling: Visualization and Polytomous Modeling of Survival and Competing Risks

A publication-ready toolkit for modern survival and competing risks analysis with a minimal, formula-based interface. Both nonparametric estimation and direct polytomous regression of cumulative incidence functions (CIFs) are supported. The main functions 'cifcurve()', 'cifplot()', and 'cifpanel()' estimate survival and CIF curves and produce high-quality graphics with risk tables, censoring and competing-risk marks, and multi-panel or inset layouts built on 'ggplot2' and 'ggsurvfit'. The modeling function 'polyreg()' performs direct polytomous regression for coherent joint modeling of all cause-specific CIFs to estimate risk ratios, odds ratios, or subdistribution hazard ratios at user-specified time points. All core functions adopt a formula-and-data syntax and return tidy and extensible outputs that integrate smoothly with 'modelsummary', 'broom', and the broader 'tidyverse' ecosystem. Key numerical routines are implemented in C++ via 'Rcpp'.

Authors:Shiro Tanaka [aut, cre, cph], Shigetaka Kobari [ctb], Chisato Honda [ctb]

cifmodeling_1.0.0.tar.gz
cifmodeling_1.0.0.tar.gz(r-4.7-arm64)cifmodeling_1.0.0.tar.gz(r-4.7-x86_64)cifmodeling_1.0.0.tar.gz(r-4.6-arm64)cifmodeling_1.0.0.tar.gz(r-4.6-x86_64)
cifmodeling_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cifmodeling/json (API)

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

Bug tracker:https://github.com/gestimation/cifmodeling/issues

Pkgdown/docs site:https://gestimation.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

4.62 score 30 scripts 264 downloads 8 exports 39 dependencies

Last updated from:29d9f3439d. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK235
linux-devel-x86_64OK293
source / vignettesOK377
linux-release-arm64OK251
linux-release-x86_64OK258
wasm-releaseOK197

Exports:cifcurvecifflowchartcifpanelcifploteffect_label.polyregEventextract_time_to_eventpolyreg

Dependencies:backportsbootbroomclicpp11dplyrfarvergenericsggplot2ggsurvfitgluegtableisobandlabelinglatticelifecyclemagrittrMatrixnleqslvpatchworkpillarpkgconfigpurrrR6RColorBrewerRcpprlangS7scalesstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Full lists of arguments
cifcurve() | cifplot() | cifpanel() | polyreg()
Rendered fromv7_arguments.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2025-12-13

Gallery
Default for survival data | Default for competing risks data | Visual elements | Risk ratio label | Panel mode | Zoomed-in-view | Font, style and palette | Axis and label | Proportional hazards check by a log-log plot
Rendered fromv5_gallery.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2025-12-13

Overview
Quick start | Why cifmodeling? | Tools for survival and competing risks analysis | Position in the survival ecosystem | Installation | Quality control
Rendered fromv1_overview.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2025-12-13

Additional information
Model formula and outcome.type in cifcurve() and polyreg() | Event coding conventions incifmodeling and CDISC ADaM ADTTE | Key arguments of msummary() that are helpful when reporting polyreg() results | Processing pipeline of cifcurve() | Processing pipeline of polyreg() | Reproducibility and conventions for polyreg()
Rendered fromv6_information.Rmdusingknitr::rmarkdown

Last update: 2025-12-13
Started: 2025-12-13

Computational formulas in cifcurve()
Formulas for the Kaplan–Meier estimator | Formulas for the Aalen-Johansen estimator | It is known that the Aalen-Johansen estimator can be expanded under regularity conditions as$$n^{1/2}{\hat F_k(t) - F_k(t)} = n^{-1/2} \sum_{i=1}^n IF_{ik}(t) + o_p(1)$$and the process $n^{1/2}{\hat F_{ik}(t) - F_{ik}(t)}$ converges weakly to a tight Gaussian process. Here $IF_{ik}(t)$ is the influence function, the contribution of $i$-th observation to the Aalen-Johansen estimator, and may be written as$$IF_{ik}(t) =\int_0^t\frac{n S(u^-)}{Y(u)},dM_i(u) | Confidence interval options
Rendered fromv3_formulas.Rmdusingknitr::rmarkdown

Last update: 2025-12-13
Started: 2025-12-13

Direct polytomous regression
Why direct modeling of CIFs? | Coherent modeling of CIFs using polytomous log odds products | 1. Nuisance model | 2. Effect measures and time points | 3. Censoring adjustment | Estimator and confidence intervals | Return
Rendered fromv2_polyreg.Rmdusingknitr::rmarkdown

Last update: 2025-12-13
Started: 2025-12-13

Examples
Example 1. Unadjusted competing risks analysis | Example 2. Survival analysis | Example 3. Adjusted competing risks analysis | Example 4. Description of cumulative incidence of competing events
Rendered fromv4_examples.Rmdusingknitr::rmarkdown

Last update: 2025-12-13
Started: 2025-12-13