v0.7.0

• Feature: The new marginal_effects() function produces marginal treatment effects, as a wrapper around absolute predictions from predict(). For example, for an analysis with a binary outcome marginal odds ratios, risk ratios, or risk differences may be produced. For survival outcomes, marginal effects may be based on the full range of predictions produced by predict(), such as marginal differences in restricted mean survival times, or time-varying marginal hazard ratios.
• Feature: Progress bars are now displayed when running interactively for calculations with predict() or marginal_effects() from ML-NMR models that may take longer to run. These can be controlled with the new progress argument.
• Deprecation: The trt_ref argument to predict() has been renamed to baseline_ref; using trt_ref is now soft-deprecated. Renaming this argument baseline_ref follows the naming convention for the other arguments (baseline_type, baseline_level) that specify the details of a provided baseline distribution. This also makes way for the new marginal_effects() functionality.
• Fix: Fallback formatting used by print methods when the crayon package is not installed now works properly, rather than giving errors.
• Fix: Small bug caused predict() for AgD meta-regression models with new data and baseline_type = "response" to fail with an error.
• Fix: The number of studies on a contrast in a network plot plot.nma_data() with weight_edges = TRUE was incorrect when a study had multiple arms of the same treatment. This now correctly counts the number of studies making a comparison, rather than the number of arms.