Changes in version 1.6.1 (2023-02-28)                  

  - Experimental: aft_mixture and aft_integrated regression models

                 Changes in version 1.5.9 (2023-01-07)                  

  - Bug fix: predict(..., type="meanhr") and type="meanhaz"

                 Changes in version 1.5.2 (2021-03-03)                  

  - Add Clayton copulas with arbitrary cluster sizes to the parametric
    GSMs (experimental)
      - Add spline interpolation support in the Markov models (faster
        for some models)

                 Changes in version 1.5.1 (2019-11-05)                  

  - Bug fix: mean predictions

                 Changes in version 1.5.0 (2019-10-15)                  

  - Major change: markov_msm function for Markov multistate models
      - Add predict(..., type="lpmatrix")
      - Add cluster and bhazard specials
      - Internal: use Nelder-Mead for AFT if the model did not converge
      - Internal: refactor stpm2 and pstpm2 to use a common gsm function
      - Internal: extended the test suite
      - Documentation: update vuniroot vignette
      - Bug fixes: delayed entry; missing bhazard and cluster values;

                 Changes in version 1.4.5 (2019-01-17)                  

  - Fixed a bug in fitting frailty models (introduced in 1.4.4)
      - Introduced package tests

                 Changes in version 1.4.4 (2018-11-01)                  

  - Fixed a critical bug in the predict function for comparisons of
    hazards, including type="hr", type="hdiff" and type="marghr"
    (introduced in 1.4.2).

                 Changes in version 1.4.2 (2018-05-29)                  

  - Belatedly started the NEWS.md file
      - Update to bbmle (>= 1.0.20) required due to new export from that
        package
      - Possible breaking change: for the predict() functions for stpm2
        and pstpm2, the keep.attributes default has changed from TRUE to
        FALSE. Any code that used predict() and needs the newdata
        attributes should now add the keep.attributes=TRUE argument. The
        previous default was noisy.
      - Possible breaking change: the derivative of the design matrix
        with respect to time now defaults to being calculated using
        log(time); the old calculation can be found using
        log.time.transform=TRUE. This is expected to provide more
        accurate gradients, particularly for very small times.
      - To this point, the following models are available:
          - stpm2: parametric generalised survival models, possibly with
            clustered data (Gamma frailties and normal random effects),
            relative survival, robust standard errors, rich
            post-estimation and plots.
          - pstpm2: penalised generalised survival models, possibly with
            clustered data (Gamma frailties and normal random effects),
            relative survival, robust standard errors, rich
            post-estimation and plots.
          - aft: parametric accelerated failure time models, with more
            limited post-estimation and plots.
      - Links for the generalised survival models include log-log,
        -logit, -probit, -log and Aranda-Ordaz.
      - Post-estimation for stpm2 and pstpm2 includes:
          - Conditional survival ("surv"), linear predictor ("link"),
            cumulative hazard ("cumhaz"), hazard ("hazard"), log hazard
            ("loghazard"), probability density function ("density"),
            failure ("fail"), hazard ratio ("hr"), survival difference
            ("sdiff"), hazard difference ("hdiff"), mean survival
            ("meansurv"), mean survival differences ("meansurvdiff"),
            mean hazard ratio ("meanhr"), odds ("odds"), odds ratio
            ("or"), restricted mean survival time ("rmst"), attributable
            fractions ("af")
          - Marginal survival ("margsurv"), marginal hazard ("marghaz"),
            attributable fractions ("af"), mean survival
            ("meanmargsurv")