control_bayes() in
the quickstart vignettemethod_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)mcse() to calculate the Monte Carlo
standard error for pooled estimates from (approximate) Bayesian
imputation. (#493)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)control_bayes() function to allow expert users to
specify additional control arguments for the MCMC computations using
rstan. (#477)lsmeans(.weights = "proportional_em")
would error if there was only a single categorical variable in the
dataset. (#412)|> and lambda functions
\(x) from code base to ensure package is backwards
compatible with older versions of R. (#474)rstan model were
not being correctly cleared (#459)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)seed argument to
method_bayes() in favour of using the base
set.seed() function (#431)rbmi
(#406)lsmeans() for better consistency with the
emmeans package (#412)
lsmeans(..., weights = "proportional") to
lsmeans(..., weights = "counterfactual")to more accurately
reflect the weights used in the calculation.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.analyse()
function (#370)mmrm package (#437)rbmi citation detail (#423 #425)impute() (#408)pkgdown
website (#433)rbmi depends on|> in testing code so package
is backwards compatible with older serversglmmTMB 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.delta_template()draws()simulate_data()
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