Changes in version 0.2.2.9000                      

Bug fixes

  - Updated MCMC step for parameter lambda to improve MCMC convergence
    for probit model

Minor changes

  - Modifications to speed up running time
    
      - Optimized the makeKpart function
    
      - Print diagnostics options moved outside of main loops

                 Changes in version 0.2.2 (2022-03-28)                  

Bug fixes

  - Corrected code that produced warning length > 1 in coercion to
    logical

  - Update functions that use deprecated functions from dplyr package

Minor changes

  - No longer export the following functions:
    
      - CalcGroupPIPs, CalcWithinGroupPIPs, and CalcPIPs as these should
        typically be calculated using the function ExtractPIPs
    
      - ComputePostmeanHnew.approx and ComputePostmeanHnew.exact as
        these should typically be calculated using the function
        ComputePostmeanHnew
    
      - set_verbose_opts as this is only called internally

  - Expanded function documentation by adding example code

                 Changes in version 0.2.1 (2022-03-04)                  

Bug fixes

  - allowable values for starting parameter for r[m] parameters updated
    as follows
    
      - no longer truncated to a single value (when varsel = FALSE and
        rmethod = "varying")
    
      - can be equal to 0 (when varsel = TRUE)

  - Error no longer generated if starting values for h.hat are not
    positive

  - When checking class of an object, use inherits() instead of class()

                 Changes in version 0.2.0 (2017-03-24)                  

Major changes

  - Added ability to have binomial outcome family by implementing probit
    regression within kmbayes()

  - Removed computation of the subject-specific effects h[i] within
    kmbayes(), as this is not always desired, and greatly slows down
    model fitting
    
      - This could still be done by setting the option est.h = TRUE in
        the kmbayes function
    
      - posterior samples of h[i] can now be obtained via the
        post-processing SamplePred function; alternatively, posterior
        summaries (mean, variance) can be obtained via the
        post-processing ComputePostmeanHnew function

  - Added ability to use exact estimates of the posterior mean and
    variance by specifying the argument method = 'exact' within the
    post-processing functions (e.g., OverallRiskSummaries(),
    PredictorResponseUnivar())

Bug fixes

  - Fixed PredictorResponseBivarLevels() when argument both_pairs = TRUE
    (#4)