NEWS
CoxBoost 1.5.1 (2025-12-11)
CoxBoost 1.5
- Latest GitHub release since the package was archived on CRAN on November 11th 2020.
CoxBoost 1.4 (2013-05-10)
- 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 (2011-11-15)
- 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 (2010-11-17)
- 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 (2010-02-06)
- 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 (2009-02-14)
- 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
CoxBoost 1.0 (2008-01-11)