v0.3.0

This release adds new features for specifying baseline distributions when producing absolute predictions, and fixes errors that occurred when specifying certain types of models with contrast-based data, along with other improvements and fixes.

• Feature: Added baseline_type and baseline_level arguments to predict.stan_nma(), which allow baseline distributions to be specified on the response or linear predictor scale, and at the individual or aggregate level.
• Feature: The baseline argument to predict.stan_nma() can now accept a (named) list of baseline distributions if newdata contains multiple studies.
• Improvement: Misspecified newdata arguments to functions like relative_effects() and predict.stan_nma() now give more informative error messages.
• Fix: Constructing models with contrast-based data previously gave errors in some scenarios (ML-NMR models, UME models, and in some cases AgD meta-regression models).
• Fix: Ensure CRAN additional checks with --run-donttest run correctly.

v0.2.1

• Fix: Producing relative effect estimates for all contrasts using relative_effects() with all_contrasts = TRUE no longer gives an error for regression models.
• Fix: Specifying the covariate correlation matrix cor in add_integration() is not required when only one covariate is present.
• Improvement: Added more detailed documentation on the likelihoods and link functions available for each data type (likelihood and link
  arguments in nma()).

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.

v0.1.3

First CRAN release.

• Formatting fixes for CRAN
• Reduced size of vignettes
• Added zenodo DOI

v0.1.0

multinma 0.1.0

• Feature: Network plots, using a plot() method for nma_data objects.
• Feature: as.igraph(), as_tbl_graph() methods for nma_data objects.
• Feature: Produce relative effect estimates with relative_effects(),
  posterior ranks with posterior_ranks(), and posterior rank probabilities with
  posterior_rank_probs(). These will be study-specific when a regression model
  is given.
• Feature: Produce predictions of absolute effects with a predict() method for
  stan_nma objects.
• Feature: Plots of relative effects, ranks, predictions, and parameter
  estimates via plot.nma_summary().
• Feature: Optional sample_size argument for set_agd_*() that:
  • Enables centering of predictors (center = TRUE) in nma() when
    a regression model is given, replacing the agd_sample_size argument of nma()
  • Enables production of study-specific relative effects, rank probabilities,
    etc. for studies in the network when a regression model is given
  • Allows nodes in network plots to be weighted by sample size
• Feature: Plots of residual deviance contributions for a model and "dev-dev"
  plots comparing residual deviance contributions between two models, using a
  plot() method for nma_dic objects produced by dic().
• Feature: Complementary log-log (cloglog) link function link = "cloglog" for
  binomial likelihoods.
• Feature: Option to specify priors for heterogeneity on the standard deviation,
  variance, or precision, with argument prior_het_type.
• Feature: Added log-Normal prior distribution.
• Feature: Plots of prior distributions vs. posterior distributions with
  plot_prior_posterior().
• Feature: Pairs plot method pairs().
• Feature: Added vignettes with example analyses from the NICE TSDs and more.
• Fix: Random effects models with even moderate numbers of studies could be very
  slow. These now run much more quickly, using a sparse representation of the RE
  correlation matrix which is automatically enabled for sparsity above 90%
  (roughly equivalent to 10 or more studies).

v0.0.1

Initial release.