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CoxBoost 1.5.1

• CRAN release.

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

• Initial public release

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.
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