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.

baggr 0.6.10-0.6.18 (Sept-Dec 2021)

• You can add numerical values to plot.baggr_compare and baggr_plot graphics (a la forest plot)
• You don’t need to convert summary data to individual-level data before running model="logit", call to baggr() should detect it automatically now
• pooling() includes extra metrics, including study weights calculation (and better documentation)
• You can now plot the objects returned by loocv() to understand out-of-sample performance graphically
• Risk difference models are now easy to fit, you only need to transform your binary input data with prepare_ma(..., effect = "RD")

Misc:

• You can plot hyperparameter values only (without group-specific estimates) in baggr_compare() now
• Removed an unnecessary dependency on the quantreg package
• Rare event corrections (prepare_ma()) can now be applied either to particular studies or all data (the literature sometimes recommends the latter)
• Clearer prompts about priors and pooling in control arms when working with individual-level data models.
• Can now set priors for error terms in linear regression models (prior_sigma)
• Added lognormal() prior and updated some prior documentation

Bug fixes:

• Print errors when examining LOO CV results
• LOO CV with full pooling and binary outcomes now works again after being broken in 0.6. Some of the results in 0.5 and 0.6 releases may have been wrong
• Individual-level Rubin model with covariates was also broken in 0.6
• Fixed a calculation of default beta prior
• No more confusing warnings about setting prior_control for "logit" model.
• binary_to_individual with non-integer number of events warns user and throws an error now
• Confusing results in baggr_binary vignette (rare events section)
• Fixes crashes for elpd calculations with unusual binary input data

baggr 0.6.5-0.6.9 (June-August 2021)

• Mu & tau models now also print correlations between effects, via a new function mutau_cor
• You can now change type of visual comparison (baggr_compare) on the fly (between "effects" and "groups"). Printing comparisons also returns posterior predictive draws.
• Upgraded forest plots to work with forestplot 2.0

Minor bug fixes:

• Fixed errors that could happen when using multiple factor covariates, or various covariate models with loocv()
• Fixed a bug with reporting wrong SD’s for effect in the v0.6 mutau model when using plot.baggr_compare
• Fixed ordering of groups in baggr_compare()
• Various small changes to reduce amount of persistent messages triggered by normal user behaviour.
• Fixed a bug where priors for meta-regressions were set even though there were no covariates.

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.

baggr 0.4.0 (February 2020)

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.

baggr 0.3.0

New features

• Binary data models for both summary and individual-level data.
• New vignette for working with binary data; see vignette("baggr_binary").
• Expanded helper functions (esp. prepare_ma), esp. for prepping binary data.
• Added forest plot functionality for all types of models.
• Various outputs can now be transformed (main use case is exp, but any transform is allowed).
• Reworked vignette sections for pooling and cross-validation.
• Pooling statistics are now calculated for the whole model and better documented.
• More consistent theming, similar to bayesplot (thanks to Brice Green)
• Comparison of leave-one-out cross-validations with loo_compare (thanks to Brice Green)

Bug fixes

• Re-enabled missing Cauchy priors

baggr 0.2.0

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.

baggr 0.1.0

First package version for CRAN.