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ncvsurv() in a pathwise manner instead of with a single value of lambda.survAUC. Ported the essential C code for computing time-dependent AUC and fixed the build issues in r-devel.This version is a major refactor of the package, with several technical adjustments to improve the functional interface, code structure, and execution performance. As a result, a few critical API-breaking changes have been made. Please update your previous code that calls hdnom accordingly. For the detailed changes, please check the updated items below.
hdcox.*() are renamed as fit_*(), hdnom.nomogram() is renamed as as_nomogram(), hdnom.validate() is renamed as validate(), and so on.rms, by reusing a minimal set of code from rms for nomogram construction and plotting. This results in clearer code structure, better maintainability, and faster package installation/loading speed. Also removed other non-essential package dependencies.print functions are now returned invisbily, to make it easier to use them in a pipe.fit$model, and the selected “optimal” hyperparameters can be accessed by fit$lambda. The model type is now stored explicitly as fit$type.as_nomogram (previously hdnom.nomogram()) now accepts the fitted model objects directly instead of the $model component. It now will recognize the model type automatically, thus the previous arguments model.type has been deprecated. so that it is easier to chain the function calls together using magrittr.as_nomogram, the previous ddist argument is not needed anymore and has been removed. There is also no more need to set a datadist object as a into the global options variable (which was required in the rms user flow).theme_hdnom() and applies it to most of the validation, calibration, and comparison plots for a consistent, cleaner look across plots within the package.glmnet.survcurve(), ncvreg.survcurve(), penalized.survcurve()) and Breslow baseline hazard estimator functions (glmnet.basesurv(), ncvreg.basesurv(), penalized.basesurv()).hdnom.calibrate().README.md.lambda1 and lambda2 instead of a single “lambda” are now required to fit, validate, and calibrate fused lasso models.lambda in hdnom.nomogram is no longer needed and has been deprecated.eps and max.iter for MCP and SCAD penalty related models. Setting the default values to be 1e-4 and 10000, which is consistent with ncvreg 3.8-0.hdnom.kmplot() under ggplot2 2.2.0, which is caused by a previous workaround for a bug introduced in ggplot2 2.1.0.max.iter for ncvsurv to a substantially higher value (5e+4).ncvsurv under ncvreg >= 3.7-0.ylim for plot.hdnom.validate(), plot.hdnom.external.validate(), and plot.hdnom.compare.validate() (#4).hdnom.compare.validate() for model comparison by validationhdnom.compare.calibrate() for model comparison by calibrationhdnom.external.validate() for external validationhdnom.external.calibrate() for external calibrationpredict and print methods for hdcox.model objectshdnom.kmplot(): Kaplan-Meier analysis for risk groups using internal/external calibration resultshdnom.logrank(): Log-rank test for risk groups using internal/external calibration resultsRhdcox.*() functions. Make examples compatible with ncvreg 3.5-0, which refined CV implementation for survival models and improved computation speed.Support five more high-dimensional penalized Cox model types:
hdnom.validate(), hdnom.calibrate(), hdcox.aenet(), and hdcox.enet() by reducing resampling times.parallel to hdcox.aenet() and hdcox.enet() to enable or disable the use of parallel parameter tuning.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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