Entire package now leverages distributions3 for object-oriented computations on distributions fitted/predicted by various kinds of models.
In particular, procast() first obtains prodist() (probability distribution) and then applies the standard methods for computing densities, probabilities, quantiles, and moments.
Similarly, proresiduals() obtains the predicted distributions and compares with the newresponse() to obtain (randomized) quantile residuals by default. Alternatively, PIT residuals or Pearson residuals as well as raw response residuals are available. The function proresiduals() also replaces both pitresiduals() and qresiduals() which were provided by earler versions of topmodels.
Via the same approach proscore() implements various kinds of scoring rules, in particular log-score (negative log-likelihood), (continuous) ranked probability score (CRPS), mean absolute error (MAE), mean squared error (MSE), and the Dawid-Sebastiani-Score (DSS). The standard log-likelihood (without sign change) is also available.
For the CRPS one can either leverage the functions from the scoringRules package (if available) or the new crps.distribution() method for numeric approximation/numeric integration to calculate the CRPS for univariate distributions. This is also used when no analytic solution is available in the scoringRules package.
The graphical functions rootogram(), pithist(), qqrplot(), wormplot(), and reliagram() are also switched to the new infrastructure based on distributions3, notably via procast() and proresiduals().
New distribution/model interfaces were added first in topmodels but some subsequently moved to other packages: GAMLSS() is now in gamlss.dist, BAMLSS() is now in bamlss, and Empirical() is still in topmodels for now.
New wrapper function promodel() that adds the class "promodel" (for probabilistic model) to an existing model object so that predict() dispatches to procast() and residuals() dispatches to proresiduals(). This facilitates using model functionality based on the standard predict() and residuals() methods like marginaleffects.
New version, presented at DAGStat 2022 and at useR! 2022 (together with distributions3).
Some conceptual changes in the generation of graphical evaluation tools for both base R and ggplot2 style graphics.
autoplot() builds now on newly written geom_*() and stat_*() functions.
First version, presented at useR! 2021.
Diagnostic graphics for Q-Q plots of randomized residuals, PIT (probability integral transform) histograms, reliability diagrams, wormplots, and rootograms. All graphical evaluations can be rendered both in base R graphics and ggplot2.
Basic probabilistic forecasting infrastructure for lm, crch, disttree and glm model classes. Not all families and forecasting types are fully supported yet.