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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()
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.