Package: ahaz 1.15

Anders Gorst-Rasmussen

ahaz: Regularization for Semiparametric Additive Hazards Regression

Computationally efficient procedures for regularized estimation with the semiparametric additive hazards regression model.

Authors:Anders Gorst-Rasmussen <[email protected]>

# Install 'ahaz' in R:

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

Uses libs:

Datasets:

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

Last updated 2 years agofrom:b256075c04. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Sep 19 2024
R-4.5-linux-x86_64	OK	Sep 19 2024

Help page	Topics
Fit semiparametric additive hazards model	ahaz
Adjusted univariate association measures from ahaz	ahaz.adjust
Partial calculation of estimating quantities used by ahaz	ahaz.partial
Tuning controls for regularization	ahaz.tune.control bic.control cv.control
Independent screening for the semiparametric additive hazards model	ahazisis
Fit penalized semiparametric additive hazards model	ahazpen
Controls for ahazpen fitting algorithm	ahazpen.fit.control
Penalty controls for ahazpen	ahazpen.pen.control lasso.control sscad.control
Plot an ahaz object	plot.ahaz
Plot an ahazpen object	plot.ahazpen
Plot a cumahaz object	plot.cumahaz
Plot a tune.ahazpen object	plot.tune.ahazpen
Prediction methods for ahaz	coef.ahaz predict.ahaz residuals.ahaz vcov.ahaz
Prediction methods for ahazpen	coef.ahazpen predict.ahazpen
Prediction methods for tune.ahazpen	coef.tune.ahazpen predict.tune.ahazpen
Print an ahazisis object	print.ahazisis
Print an ahazpen object	print.ahazpen summary.ahazpen
Print a summary.ahaz object	print.summary.ahaz
Print a tune.ahazpen object	print.tune.ahazpen
Sorlie gene expressions	sorlie
Summarize an ahaz object	summary.ahaz
Choice of penalty parameter in ahazpen	tune.ahazpen