DHARMa

Major changes

• For hierarchical models (GLMMs), we changed the default simulations from the supported model's default (mostly unconditional) to conditional simulations (for most of the supported models and packages). This represents a major change in the calculated scaled residuals and is implemented to ensure higher power in dispersion and other tests. We expect different results for calculated residuals using this or older DHARMa versions. For compatibility, you can use the argument simulateREs = "user-specified" to change back to the model's default (older DHARMa versions). See package vignette for more details.
• In plotResiduals, we increased the threshold for the automatic change from quantile regression lines to the spline: from 2,000 to 10,000 data points. When it happens, a message is displayed to warn users. Also, when quantreg = F, the color of the spline was changed to black because there is no test associated with the line (as there is for quantile regression).
• In plotResiduals, the argument form has a new functionality. Beyond the syntax data$predictor, it allows now to use the formula structure ~predictor, ~predictor1+predictor2, ~., ~predictor|group and ~predictor|group == "group_level" to plot the residuals against specific/all predictors and grouping variables/levels. This works for most of the supported model functions. It solves problems with NAs in datasets that were excluded by the model. #407 / #425
• In testCategorical, recalculateResiduals, testQuantiles, testSpatialAutocorrelation and testTemporalAutocorrelation, additional predictors (catPred, group, sel, predictor, time, x,y- respectively) can now be specified as a formula (similar to plotResiduals). This handles rows with NAs that were excluded by the model automatically.
• brms is now supported by DHARMa for simple models, i.e. models that could also be fit using glmmTMB (no multi-response, multinomial or structural equation models).

Bugfixes

• Fixing error in runBenchmarks.
• Fixing inconsistency in testQuantiles() #465. Including rankTransform() help function.

Minor changes

• Using 95% confidence intervals for confidence bands in plots for testQuantiles(). Before, we were using standard errors (~ 68% CI).
• Vectorize randomization of residuals to improve speed - PR#493 contributed by StaffanBetner.