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Published June 23, 2020 | Version v0.1.0
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dmphillippo/multinma v0.1.0

Authors/Creators

Description

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).

Files

dmphillippo/multinma-v0.1.0.zip

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Additional details

Related works

Is supplement to
https://github.com/dmphillippo/multinma/tree/v0.1.0 (URL)