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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