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.