rbmi 1.5.0
New Features
- All covariance structures are now also supported for Bayesian multiple imputation:
method_bayes()gained additionalcovarianceandprior_covarguments to allow users to specify the covariance structure and prior for the Bayesian imputation model. Please see the updated statistical specifications vignette for details. (#501, #518) - New function
mcse()to calculate the Monte Carlo standard error for pooled estimates from (approximate) Bayesian imputation. (#493)
rbmi 1.4.0
CRAN release: 2025-02-07
Breaking Changes
- Deprecated the
burn_inandburn_betweenarguments inmethod_bayes()in favour of using thewarmupandthinarguments, respectively, in the newcontrollist produced bycontrol_bayes. This is to align with therstanpackage. (#477)
New Features
- Added
control_bayes()function to allow expert users to specify additional control arguments for the MCMC computations usingrstan. (#477)
rbmi 1.3.1
CRAN release: 2024-12-11
- Fixed bug where stale caches of the
rstanmodel were not being correctly cleared (#459)
rbmi 1.3.0
CRAN release: 2024-10-16
Breaking Changes
- Convert
rstanto be a suggested package to simplify the installation process. This means that the Bayesian imputation functionality will not be available by default. To use this feature, you will need to installrstanseparately (#441) - Deprecated the
seedargument tomethod_bayes()in favour of using the baseset.seed()function (#431)
New Features
- Added vignette on how to implement retrieved dropout models with time-varying intercurrent event (ICE) indicators (#414)
- Added vignette on how to obtain frequentist and information-anchored inference with conditional mean imputation using
rbmi(#406) - Added FAQ vignette including a statement on validation (#407 #440)
- Updates to
lsmeans()for better consistency with theemmeanspackage (#412)- Renamed
lsmeans(..., weights = "proportional")tolsmeans(..., weights = "counterfactual")to more accurately reflect the weights used in the calculation. - Added
lsmeans(..., weights = "proportional_em")which provides consistent results withemmeans(..., weights = "proportional") -
lsmeans(..., weights = "proportional")has been left in the package for backwards compatibility and is an alias forlsmeans(..., weights = "counterfactual")but now gives a message prompting users to use either “proptional_em” or “counterfactual” instead.
- Renamed
- Added support for parallel processing in the
analyse()function (#370) - Added documentation clarifying potential false-positive warnings from rstan (#288)
- Added support for all covariance structures supported by the
mmrmpackage (#437) - Updated
rbmicitation detail (#423 #425)
rbmi 1.2.6
CRAN release: 2023-11-24
- Updated unit tests to fix false-positive error on CRAN’s testing servers
rbmi 1.2.5
CRAN release: 2023-09-20
- Updated internal Stan code to ensure future compatibility (@andrjohns, #390)
- Updated package description to include relevant references (#393)
- Fixed documentation typos (#393)
rbmi 1.2.3
CRAN release: 2022-11-14
- Minor internal tweaks to ensure compatibility with the packages
rbmidepends on
rbmi 1.2.1
CRAN release: 2022-10-25
- Removed native pipes
|>in testing code so package is backwards compatible with older servers - Replaced our
glmmTMBdependency with themmrmpackage. This has resulted in the package being more stable (less model fitting convergence issues) as well as speeding up run times 3-fold.
rbmi 1.1.4
CRAN release: 2022-05-18
- Updated urls for references in vignettes
- Fixed a bug where visit factor levels were re-constructed incorrectly in
delta_template() - Fixed a bug where the wrong visit was displayed in the error message for when a specific visit doesn’t have any data in
draws() - Fixed a bug where the wrong input parameter was displayed in an error message in
simulate_data()
