# topmodels 0.3-0

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()`

.The pointwise and simultaneous confidence intervals in

`rootogram()`

now rely on the exact`PoissonBinomial()`

distribution (now available in`distributions3`

) rather than its binomial approximation.In addition to pointwise and simultaneous confidence intervals for

`rootogram()`

,`"tukey"`

confidence intervals are now available which simply correspond to limits of -1 and 1 for hanging or suspended rootograms. For other flavors of rootograms these limits are transformed correspondingly.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`

.

# topmodels 0.2-0

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.

# topmodels 0.1-0

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.