rbmi 1.5.1

Bug Fixes

All covariance structures are now also supported for Bayesian multiple imputation: method_bayes() gained additional covariance and prior_cov arguments 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)

Fixed cluster used in parallel test and make sure tests clean up Stan files properly. (#523)
Small updates and fixes to documentation. (#504, #506, #498)

Fixed Stan related bug that caused unit tests to fail on machines compiling with the C23 standard (#481)
Fixed bug in unit test that caused false-positive reproducibility errors (#483)

Deprecated the burn_in and burn_between arguments in method_bayes() in favour of using the warmup and thin arguments, respectively, in the new control list produced by control_bayes. This is to align with the rstan package. (#477)

Added control_bayes() function to allow expert users to specify additional control arguments for the MCMC computations using rstan. (#477)

Fixed bug where lsmeans(.weights = "proportional_em") would error if there was only a single categorical variable in the dataset. (#412)
Removed native pipes |> and lambda functions \(x) from code base to ensure package is backwards compatible with older versions of R. (#474)

Fixed bug where stale caches of the rstan model were not being correctly cleared (#459)

Convert rstan to 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 install rstan separately (#441)
Deprecated the seed argument to method_bayes() in favour of using the base set.seed() function (#431)

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 the emmeans package (#412)
- Renamed lsmeans(..., weights = "proportional") to lsmeans(..., weights = "counterfactual")to more accurately reflect the weights used in the calculation.
- Added lsmeans(..., weights = "proportional_em") which provides consistent results with emmeans(..., weights = "proportional")
- lsmeans(..., weights = "proportional") has been left in the package for backwards compatibility and is an alias for lsmeans(..., weights = "counterfactual") but now gives a message prompting users to use either “proptional_em” or “counterfactual” instead.
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 mmrm package (#437)
Updated rbmi citation detail (#423 #425)

Stopped warning messages being accidentally supressed when changing the ICE type in impute() (#408)
Fixed equations not rendering properly in the pkgdown website (#433)

Minor internal tweaks to ensure compatibility with the packages rbmi depends on

Removed native pipes |> in testing code so package is backwards compatible with older servers
Replaced our glmmTMB dependency with the mmrm package. This has resulted in the package being more stable (less model fitting convergence issues) as well as speeding up run times 3-fold.

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()

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