- 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.

- 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()`

).

- 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.

- Format DESCRIPTION to CRAN requirements

- Wrapped long-running examples in
`\donttest{}`

instead of`\dontrun{}`

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

- 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

- Enables centering of predictors (
- 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).

- Initial release.