Changes in version 1.5.1 (2025-12-11)                  

  - CRAN release.

                         Changes in version 1.5                         

  - Latest GitHub release since the package was archived on CRAN on
    November 11th 2020.

                  Changes in version 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

                  Changes in version 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

                        Changes in version 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

                 Changes in version 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

                  Changes in version 1.2 (2010-02-06)                   

  - Added function estimPVal() for permutation-based p-value estimation
  - Improved the speed of the penalty updating code in PathBoost

                        Changes in version 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

                  Changes in version 1.1 (2009-02-14)                   

  - Implemented penalty modification factors and penalty change
    distribution via a connection matrix
  - Implemented estimation of models for competing risks

                        Changes in version 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

                  Changes in version 1.0 (2008-01-11)                   

  - Initial public release