CoxBoost 1.5.1
CoxBoost 1.5
- 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
CoxBoost 1.0