Package: pseudoCure 1.0.0

pseudoCure: A Pseudo-Observations Approach for Analyzing Survival Data with a Cure Fraction

A collection of easy-to-use tools for regression analysis of survival data with a cure fraction proposed in Su et al. (2022) <doi:10.1177/09622802221108579>. The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard (promotion time cure) model. The pseudo-observations approach is utilized to assess covariate effects and embedded in the variable selection procedure.

Authors:Sy Han Chiou [aut, cre], Chien-Lin Su [aut], Feng-Chang Lin [aut]

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pseudoCure.pdf |pseudoCure.html✨
pseudoCure/json (API)

# Install 'pseudoCure' in R:
install.packages('pseudoCure', repos = c('https://stc04003.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stc04003/pseudocure/issues

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

On CRAN:

openblascpp

3.60 score 5 exports 72 dependencies

Last updated 15 days agofrom:3e1e3a77f7. Checks:11 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 07 2025
R-4.5-win-x86_64OKFeb 09 2025
R-4.5-mac-x86_64OKFeb 07 2025
R-4.5-mac-aarch64OKFeb 07 2025
R-4.5-linux-x86_64OKFeb 07 2025
R-4.4-win-x86_64OKFeb 07 2025
R-4.4-mac-x86_64OKFeb 07 2025
R-4.4-mac-aarch64OKFeb 07 2025
R-4.3-win-x86_64OKFeb 07 2025
R-4.3-mac-x86_64OKFeb 07 2025
R-4.3-mac-aarch64OKFeb 07 2025

Exports:geelmkmmzTestpCurepCure.control

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr