Latest GitHub release since the package was archived on CRAN on
November 11th 2020.
CoxBoost 1.4
Added a formula interface through iCoxBoost
Added generic function coef for extracting estimated
coefficients
Added a plot routine that provides coefficient paths
Added support for package parallel (removing support
for multicore and older R versions)
Convergence problems for unpenalized covariates now are caught
CoxBoost 1.3
Added option criterion to allow for selection according
to unpenalized scores
Added criterion="hpscore" and
criterion="hscore" for heuristic evaluation of only a
subset of covariates in each boosting step
Fixed a bug where results from predict() without
"newdata" and "linear.predictor" in CoxBoost
objects would have the wrong order (introduced in 1.2-1)
Added missing value check for covariate matrix
Implemented observation weights
CoxBoost 1.2-2
Fixed a bug in the predict function occurred when all coefficients
were equal to zero
Fixed bug where estimPVal with using only one boosting
step
estimPVal now also works for zero boosting steps
CoxBoost 1.2-1
Improved speed of the core selection routine
Added faster code for the special case of binary covariate data
Added an option for not returning the matrix with the score
statistics for saving memory in applications with a huge number of
covariates
Optimized memory usage for a large number of covariates
Covariates with standard deviation equal to zero now only are
centered
A matrix of the employed penalties know is only stored if the
penalties, changed. Otherwise the ‘element’ penalty is just a
vector
Added support for multicore package for
cross-validation and p-value estimation
Added an option for fitting on subsets of observations
The coefficient matrix is now stored as a sparse matrix, employing
package Matrix
Fixed the implementation of the p-value estimation
CoxBoost 1.2
Added function estimPVal() for permutation-based
p-value estimation
Improved the speed of the penalty updating code in PathBoost
CoxBoost 1.1-1
fixed bug in print method (introduced in 1.0-1) where the number of
non-zero coefficients would be taken from a wrong boosting step
CoxBoost 1.1
Implemented penalty modification factors and penalty change
distribution via a connection matrix
Implemented estimation of models for competing risks
CoxBoost 1.0-1
Implemented data adaptive rule for default penalty value
Fixed bug where output of the selected covariate would print the
wrong name in presence of unpenalized covariates
Boosting now starts a step 0, i.e., also the model before updating
any of the coefficients of the penalized covariates is considered.
However, the unpenalized covariates will already have non-zero values in
boosting step 0. This change breaks code that relies on the size of
elements "coefficients", "linear.predictors",
or "Lambda" of CoxBoost objects
Implemented parallel evaluation of cross-validation folds, via
package snowfall
Speed improvements by replacing ‘apply’ and ‘rbind’, most noticeably
for a large number of observations with a small number of
covariates