v0.7.8

Binaries are available on CRAN

baggr 0.7.8 (late 2023)

New functionality:

• You can run meta-analyses with just one row of data, but must specify priors
• baggr_plot can be made to look more like forest plot with baggr_plot(bg, style = "forest")
• Plotting baggr and baggr_compare objects now has more powerful add_values functionality.
• You can customise colour of hypermean, e.g. baggr_plot(bg, hyper = "red")
• For meta-regressions, you can draw a bubble()

Back end and minor changes:

• Various CRAN and rstan bugfixes. Now that rstan 2.26 is on CRAN, this package requires users to update to it.
• A few "quality of life"-type and presentation upgrades.
• You can print hyperparameters of a model using hypermean() and hypersd() (defaults to summaries)
• Printing baggr models now also includes correlations and the posterior predictive distribution.
• loo_compare now has better annotation

Bugfixes:

• Forest plots now display correctly even if you don't label studies/groups

baggr 0.7.4 (late 2022)

• Faster code: all models should now run by up to 50% faster
• For standardised data, where mean in control group is by definition 0, you can now say pooling_control = "remove" when calling baggr(). This will avoid estimating parameters which are known to be 0.
• Predicting effects for new data: for models with covariates you can use effect_draw(object, newdata = ...) or (equivalently) predict(object, newdata = ...)
  to generate predictions for any number of new samples
• I updated the calculation of the pooling metric so that it's comparable with frequentist packages. See the help file.

Misc:

• More information when printing models.
• Baggr automatically checks for a grouping column.
• For binary data, you can run baggr() without any extra steps like prepare_ma(), by just defining effect when running baggr (or it will default to log OR).
• I added alias posterior_predict() for drawing from posterior
  sample. This is more consistent with regression modeling and RStan ecosystem.

Bugs:

• Transforms of samples on baggr_compare plots previously didn't work for some plots. This is now fixed.
• Fixed a bug when covariates didn't work for some types of summary-level binary data.

baggr 0.6.21 (January-March 2022)

Misc:

• Printing baggr and baggr_compare objects is now better at showing intervals and you can also change their widths with arguments passed to print.baggr() or directly to baggr_compare()
• Added student_t() and lognormal() priors and updated some prior documentation
• Removed some cases where input data would be reordered (previously this could happen to either individual-level continuous data or summary data of binary events)
• More warning prompts at various stages of model fitting
• Faster installation and package checks.

v0.6.4

Binaries are available on CRAN

baggr 0.6.3-0.6.4 (May 2021)

• Various documentation fixes for re-submission of v0.6 to CRAN (first one since v0.4).
• Added summary option for effect_draw.
• Factor covariates will work (better) now.
• Removed some non-essential code for faster compilation on CRAN.

baggr 0.6.2 (April 2021)

• New "mutau_full" model is a generalisation of the "mutau" model into individual-level data.
  The idea is similar as for the recent "rubin_full" changes, see version 0.6.0.

• I also reparameterised the mutau model. It should be faster and have fewer divergent
  transition warnings.Some of the code around the mu and tau model has also been
  rewritten on the back end.

On the back end the package now follows the rstantools recommended way of compiling models.
The user experience should be exactly the same, but this may avoid some problems
when installing the package from GitHub or otherwise compiling it locally.

baggr 0.6.0 (February 2021)

New features

• Spike and slab model can be called via model="sslab". See ?baggr for basics of
  working with this type of a model. A vignette will be added soon.
• Rubin model with full data is now called via model="rubin_full" rather than "full".
  Old syntax will still work, however. Made some documentation and code improvements
  around this issue.
• Leave-one-out cross-validation works for model="rubin_full" now. It works the same
  way as for model="logit". See ?baggr for more information on how to use it.
• It's now possible to use model="rubin" with the same inputs as model="mutau".
  Some data columns are removed automatically in that case.

For v0.6 we added more generic code around plotting, printing, grabbing treatment effects
etc. While there are no differences on the front-end, this means that for the next
versions we will be able to consider some new models and have more homogeneous syntax
for all models.

Bugs

• Fixed a few issues with formatting data for individual-level data models.
• Fixed a major bug with distributions of baselines in the rubin_full (full) model.
• Fixed glitchy display for some baggr_compare plots.

baggr 0.5.0 (June 2020)

New features

• Fixed and random effects for baggr models now have their own separate functions,
  fixed_effects and random_effects, in addition to group_effects
• LOO CV works for the logistic model (as does general cross-validation).
• Vignette for binary data analysis has been rewritten in parts.
• L'Abbe plots for binary data, see labbe().
• There is now more automatic conversion between summary-level and individual-level
  data for binary data (e.g. you can run baggr() with summary data and model="logit"
  for automatic conversion)
• For logistic model, priors can be specified for rates of events in the control arm,
  see arguments prior_control and prior_control_sd in baggr()
• There are experimental features for working with models of quantiles.
  We advise against fitting such models using the package until these features
  have been fully tested and documented.

Bug fixes

• Fixed some issues with printing of coefficients in meta-regressions,
  where wrong values were given for some models.

CRAN v0.4 release

baggr 0.4.0 (February 2020)

Binaries are available at CRAN

New features

• Covariates can now be used in all baggr() models: in "rubin" model they give meta-regression
  (group-level covariates), while in "full" and "logit" models they can be used for "regular"
  regression (individual-level covariates)
• Priors for covariates are set through the argument prior_beta
• You can work with regression coefficients for covariates
  • you can access and summarise coefficients through fixed_effects(),
  • you will also see them when printing baggr objects;
  • when using forest_plot() you can request show = "covariates"
• Prototype of pp_check() function now works for Rubin model (thanks to Brice Green)
  you can apply it to generate new datasets according to posterior distribution of treatment effect
  and contrast them with the observed quantities as part of model checking
• baggr_compare() function now has standard output which you can print() or plot(),
  thanks to Brice Green
• Vignettes and documentation were updated to better describe binary data analysis
• We now give more warnings when plugging in stupid inputs.

Bug fixes

• Messages for setting priors were accidentally given when e.g. running full pooling models
• All models were re-written to standardise our approach and syntax.
  • "Full" model might now behave differently.
  • "Mutau" model will be re-written and generalised for next release.
  • LOO CV is also disabled for some models. Prompts will be given.

v0.2.0 (CRAN)

New features

• Users can now define priors in baggr() using a syntax similar to rstanarm.
  Extra priors are available
• baggr() outputs prior predictive distributions; they can be examined using
  baggr_compare and effect_plot, effect_draw -- 2 new functions
• More types of model comparisons are possible
• LOO CV function has been reworked
• Full pooling and no pooling models have been reworked to avoid divergent
  transitions.

First CRAN version

Available at https://CRAN.R-project.org/package=baggr