Package: pseudoCure 1.0.0

Sy Han (Steven) Chiou

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]

pseudoCure_1.0.0.tar.gz
pseudoCure_1.0.0.tar.gz(r-4.5-noble)pseudoCure_1.0.0.tar.gz(r-4.4-noble)
pseudoCure_1.0.0.tgz(r-4.4-emscripten)pseudoCure_1.0.0.tgz(r-4.3-emscripten)
pseudoCure.pdf |pseudoCure.html✨
pseudoCure/json (API)

# Install 'pseudoCure' in R:
install.packages('pseudoCure', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.70 score 5 exports 72 dependencies

Last updated 14 days agofrom:b151166f1e. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 06 2025
R-4.5-linux-x86_64OKFeb 06 2025

Exports:geelmkmmzTestpCurepCure.control

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr