This package is considered a duplicate. The official version of this package is found at:https://nanxstats.r-universe.dev/hdnom

Package: hdnom 6.0.4

Nan Xiao

hdnom: Benchmarking and Visualization Toolkit for Penalized Cox Models

Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data.

Authors:Nan Xiao [aut, cre], Qing-Song Xu [aut], Miao-Zhu Li [aut], Frank Harrell [ctb], Sergej Potapov [ctb], Werner Adler [ctb], Matthias Schmid [ctb]

hdnom_6.0.4.tar.gz
hdnom_6.0.4.tar.gz(r-4.5-noble)hdnom_6.0.4.tar.gz(r-4.4-noble)
hdnom_6.0.4.tgz(r-4.4-emscripten)hdnom_6.0.4.tgz(r-4.3-emscripten)
hdnom.pdf |hdnom.html✨
hdnom/json (API)
NEWS

# Install 'hdnom' in R:

install.packages('hdnom', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nanxstats/hdnom/issues

Uses libs:

openblas– Optimized BLAS

Datasets:

smart - Imputed SMART study data
smarto - Original SMART study data

5.26 score 1 packages 67 scripts 498 downloads 9 mentions 26 exports 40 dependencies

Last updated 3 months agofrom:5a77cedaee. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 05 2024
R-4.5-linux-x86_64	OK	Nov 05 2024

Exports:as_nomogram calibrate calibrate_external compare_by_calibrate compare_by_validate fit_aenet fit_alasso fit_enet fit_flasso fit_lasso fit_mcp fit_mnet fit_scad fit_snet glmnet_basesurv glmnet_survcurve infer_variable_type kmplot logrank_test ncvreg_basesurv ncvreg_survcurve penalized_basesurv penalized_survcurve theme_hdnom validate validate_external

Dependencies:cli codetools colorspace fansi farver foreach ggplot2 glmnet glue gridExtra gtable isoband iterators labeling lattice lifecycle magrittr MASS Matrix mgcv munsell ncvreg nlme penalized pillar pkgconfig R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang scales shape survival tibble utf8 vctrs viridisLite withr

An Introduction to hdnom

Rendered fromhdnom.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-04-24
Started: 2015-08-28

Citation

Readme and manuals

Help Manual

Help page	Topics
Construct nomogram ojects for high-dimensional Cox models	as_nomogram
Calibrate high-dimensional Cox models	calibrate
Externally calibrate high-dimensional Cox models	calibrate_external
Compare high-dimensional Cox models by model calibration	compare_by_calibrate
Compare high-dimensional Cox models by model validation	compare_by_validate
Model selection for high-dimensional Cox models with adaptive elastic-net penalty	fit_aenet
Model selection for high-dimensional Cox models with adaptive lasso penalty	fit_alasso
Model selection for high-dimensional Cox models with elastic-net penalty	fit_enet
Model selection for high-dimensional Cox models with fused lasso penalty	fit_flasso
Model selection for high-dimensional Cox models with lasso penalty	fit_lasso
Model selection for high-dimensional Cox models with MCP penalty	fit_mcp
Model selection for high-dimensional Cox models with Mnet penalty	fit_mnet
Model selection for high-dimensional Cox models with SCAD penalty	fit_scad
Model selection for high-dimensional Cox models with Snet penalty	fit_snet
Breslow baseline hazard estimator for glmnet objects	glmnet_basesurv
Survival curve prediction for glmnet objects	glmnet_survcurve
Extract information of selected variables from high-dimensional Cox models	infer_variable_type
Kaplan-Meier plot with number at risk table for internal calibration and external calibration results	kmplot
Log-rank test for internal calibration and external calibration results	logrank_test
Breslow baseline hazard estimator for ncvreg objects	ncvreg_basesurv
Survival curve prediction for ncvreg objects	ncvreg_survcurve
Breslow baseline hazard estimator for penfit objects	penalized_basesurv
Survival curve prediction for penfit objects	penalized_survcurve
Plot calibration results	plot.hdnom.calibrate
Plot external calibration results	plot.hdnom.calibrate.external
Plot model comparison by calibration results	plot.hdnom.compare.calibrate
Plot model comparison by validation results	plot.hdnom.compare.validate
Plot nomogram objects	plot.hdnom.nomogram
Plot optimism-corrected time-dependent discrimination curves for validation	plot.hdnom.validate
Plot time-dependent discrimination curves for external validation	plot.hdnom.validate.external
Make predictions from high-dimensional Cox models	predict.hdnom.model
Print calibration results	print.hdnom.calibrate
Print external calibration results	print.hdnom.calibrate.external
Print model comparison by calibration results	print.hdnom.compare.calibrate
Print model comparison by validation results	print.hdnom.compare.validate
Print high-dimensional Cox model objects	print.hdnom.model
Print nomograms objects	print.hdnom.nomogram
Print validation results	print.hdnom.validate
Print external validation results	print.hdnom.validate.external
Imputed SMART study data	smart
Original SMART study data	smarto
Summary of calibration results	summary.hdnom.calibrate
Summary of external calibration results	summary.hdnom.calibrate.external
Summary of model comparison by calibration results	summary.hdnom.compare.calibrate
Summary of model comparison by validation results	summary.hdnom.compare.validate
Summary of validation results	summary.hdnom.validate
Summary of external validation results	summary.hdnom.validate.external
Plot theme (ggplot2) for hdnom	theme_hdnom
Validate high-dimensional Cox models with time-dependent AUC	validate
Externally validate high-dimensional Cox models with time-dependent AUC	validate_external