mlr3proba 0.4.3

• Massive speed-up in distrcompositor PipeOp/pipeline
• More informative error given if $distr called for a learner that does not support this return type
• Fix massive bottleneck in scoring rule measures
• Add Density coercions as_task_dens and as_prediction_dens
• Measures now use parameter sets like learners. This streamlines the interface but unfortunately means ids can no longer be set dynamically.
• Add parameters t_max and p_max to Graf, Schmid and Integrated Log-loss as an alternative to times. t_max is equivalent to times = seq(t_max) and p_max is the proportion of censoring to integrate up to in the dataset.
• Fix bug in Rcpp code that was causing erroneous values for calculating the cindex in datasets greater than 20,000 observations.