multinma 0.1.3

• Format DESCRIPTION to CRAN requirements

multinma 0.1.2

• Wrapped long-running examples in instead of

multinma 0.1.1

• Reduced size of vignettes
• Added methods paper reference to DESCRIPTION
• Added zenodo DOI

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).

multinma 0.0.1

• Initial release.