# 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