v0.2.0

This release adds new features, including inline variable transformations, ordered multinomial models, and flat prior distributions, along with a host of improvements and fixes.

• Feature: The set_*() functions now accept dplyr::mutate() style semantics, allowing inline variable transformations.
• Feature: Added ordered multinomial models, with helper function multi() for specifying the outcomes. Accompanied by a new data set hta_psoriasis and vignette.
• Feature: Implicit flat priors can now be specified, on any parameter, using flat().
• Improvement: as.array.stan_nma() is now much more efficient, meaning that many post-estimation functions are also now much more efficient.
• Improvement: plot.nma_dic() is now more efficient, particularly with large numbers of data points.
• Improvement: The layering of points when producing "dev-dev" plots using plot.nma_dic() with multiple data types has been reversed for improved clarity (now AgD over the top of IPD).
• Improvement: Aggregate-level predictions with predict() from ML-NMR / IPD regression models are now calculated in a much more memory-efficient manner.
• Improvement: Added an overview of examples given in the vignettes.
• Improvement: Network plots with weight_edges = TRUE no longer produce legends with non-integer values for the number of studies.
• Fix: plot.nma_dic() no longer gives an error when attempting to specify .width argument when producing "dev-dev" plots.