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sdmTMB 0.5.0
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@seananderson seananderson released this 03 Apr 20:06
v0.5.0
ad5db93

  • Overhaul residuals vignette ('article')
    https://pbs-assess.github.io/sdmTMB/articles/web_only/residual-checking.html
    including brief intros to randomized quantile residuals, simulation-based
    residuals, 'one-sample' residuals, and uniform vs. Gaussian residuals.

  • Add check if prediction coordinates appear outside of fitted coordinates. #285

  • Fix memory issue with Tweedie family on large datasets. #302

  • Add experimental option to return standard normal residuals from
    dharma_residuals().

  • Make simulate.sdmTMB() not include extra_time elements.

  • Improved re-initialization of saved fitted model objects in new sessions.

  • Fix important bug in simulate.sdmTMB() method for delta families where
    the positive linear predictor was only getting simulated for observations
    present in the fitted data.

  • Add new "mle-mvn" type to residuals.sdmTMB() and make it the default.
    This is a fast option for evaluating goodness of fit that should be better
    than the previous default. See the details section in ?residuals.sdmTMB
    for details. The previous default is now called "mvn-eb" but is not
    recommended.

  • Bring dharma_residuals() back over from sdmTMBextra to sdmTMB. Add a new
    option in the type argument ("mle-mvn") that should make the
    simulation residuals consistent with the expected distribution.
    See the same new documentation in ?residuals.sdmTMB. The examples
    in ?dharma_residuals illustrate suggested use.

  • Fix bug in sanity() where gradient checks were missing abs() such that
    large negative gradients weren't getting caught. #324

  • Return offset vector in fitted object as an element. Ensure any extra time
    rows of data in the data element of the fitted object do not include the
    extra time slices.

  • Add experimental residuals option "mle-mvn" where a single approximate
    posterior sample of the random effects is drawn and these are combined
    with the MLE fixed effects to produce residuals. This may become the
    default option.

  • Add the generalized gamma distribution (thanks to J.T. Thorson with additional
    work by J.C. Dunic.) See gengamma(). This distribution is still in a testing
    phase and is not recommended for applied use yet. #286

  • Detect possible issue with factor(time) in formula if same column name is used
    for time and extra_time is specified. #320

  • Improve sanity() check output when there are NA fixed effect standard
    errors.

  • Set intern = FALSE within index bias correction, which seems to be
    considerably faster when testing with most models.

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