The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
_R_USE_STRICT_R_HEADERS_ (upcoming default in R 4.5.0)
(#13):
STRICT_R_HEADERS.Calloc and Free with
R_Calloc and R_Free.float.h for FLT_EPSILON.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.
Health stats visible at Monitor.