Package: hdcuremodels 0.0.5

Kellie J. Archer

hdcuremodels: High-Dimensional Cure Models

Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.

Authors:Han Fu [aut], Kellie J. Archer [aut, cre], Tung Lam Nguyen [rev], Panagiotis Papastamoulis [rev]

hdcuremodels_0.0.5.tar.gz
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hdcuremodels.pdf |hdcuremodels.html
hdcuremodels/json (API)

# Install 'hdcuremodels' in R:
install.packages('hdcuremodels', repos = c('https://packages.ropensci.org', 'https://cloud.r-project.org'))

Reviews:rOpenSci Software Review #692

Bug tracker:https://github.com/kelliejarcher/hdcuremodels/issues

Datasets:

On CRAN:

Conda:

6.12 score 7 scripts 17k downloads 10 exports 98 dependencies

Last updated from:2253468146 (on main). Checks:1 ERROR, 9 OK. Indexed: yes.

TargetResultTotal timeArtifact
linux-devel-x86_64ERROR296
pkgdown docsOK256
source / vignettesOK354
linux-release-x86_64OK374
macos-release-arm64OK307
macos-oldrel-arm64OK270
windows-develOK386
windows-releaseOK452
windows-oldrelOK439
wasm-releaseOK159

Exports:auc_mcmconcordance_mcmcure_estimatecureemcuregmifscv_cureemcv_curegmifsgenerate_cure_datanonzerocure_testsufficient_fu_test

Dependencies:abindassertthatbackportsbbmlebdsmatrixBHbootbroomcarcarDataclicodetoolscorpcorcorrplotcowplotcpp11data.tableDerivdeSolvedoBydoParalleldplyrfarverfastGHQuadflexsurvflexsurvcureforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifglmnetgluegridExtragtablegtoolsisobanditeratorsknockofflabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmstatemuhazmvnfastmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpolynompurrrquadprogquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackRdsdpreformulasrlangRSpectrarstatixrstpm2S7scalesshapeSparseMstatmodstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Estimating Cure Models in High Dimensions: A Guide with hdcuremodels

Rendered fromhdcuremodels.Rmdusingknitr::rmarkdownon Oct 02 2025.

Last update: 2025-09-12
Started: 2025-03-21

Readme and manuals

Help Manual

Help pageTopics
AML test dataamltest
AML training dataamltrain
AUC for cure prediction using mean score imputationauc_mcm
Extract model coefficients from a fitted mixturecure objectcoef.mixturecure coefficients
C-statistic for mixture cure modelsconcordance_mcm
Estimate cured fractioncure_estimate
Fit penalized mixture cure model using the E-M algorithmcureem
Fit penalized parametric mixture cure model using the GMIFS algorithmcuregmifs
Fit penalized mixture cure model using the E-M algorithm with cross-validation for parameter tuningcv_cureem
Fit a penalized parametric mixture cure model using the generalized monotone incremental forward stagewise (GMIFS) algorithm (Hastie et al 2007) with cross-validation for model selectioncv_curegmifs
Dimension method for mixturecure objectsdim.mixturecure
Return model family and fitting algorithm for mixturecure model fitsfamily.mixturecure
Extract model formula for mixturecure objectformula.mixturecure
Simulate data under a mixture cure modelgenerate_cure_data
Log-likelihood for fitted mixture cure modellogLik.mixturecure
Number of observations in mixturecure objectnobs.mixturecure
Non-parametric test for a non-zero cured fractionnonzerocure_test
Number of parameters in fitted mixture cure modelnpar_mixturecure
Plot fitted mixture cure modelplot.mixturecure
Predicted probabilities for susceptibles, linear predictor for latency, and risk class for latency for mixture cure fitpredict.mixturecure
Print the contents of a mixture cure fitted objectprint.mixturecure
Test for sufficient follow-upsufficient_fu_test
Summarize a fitted mixture cure objectsummary.mixturecure