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pool() gains a rubin_df_method argument.
The default "barnard-rubin" uses the Barnard-Rubin
small-sample degrees-of-freedom adjustment, while "rubin"
uses the original approximation from Rubin (1987). (#587)ancova() now supports the analysis of two or more
treatment groups. For more than two groups the ref /
alt naming scheme is extended with alt2,
alt3, etc. based on the factor levels of
vars$group; the two-group output is unchanged
(trt, lsm_ref, lsm_alt) for
backwards compatibility. set_vars() gained a
group_contrasts argument allowing users to specify a
bespoke set of treatment-group contrasts for ancova(). By
default a contrast of each non-reference group versus the reference
group is estimated. Custom contrasts must be named (the name becomes the
output parameter name) and may be given as pairwise
c(minuend, subtrahend) character vectors or as general
named numeric weight vectors over the group levels
(e.g. c(Placebo = -1, A = 0.5, B = 0.5) for a pooled
comparison); contrasts are evaluated in a contrast-coding-agnostic way.
The data.frame produced by as.data.frame() /
pool() now includes explicit estimate_type,
group, group_level_1,
group_level_2, contrast_label and
visit columns when the analysis was performed with
ancova(). The existing parameter column is
retained for backwards compatibility. (#520)ancova() now rejects interactions involving the
treatment group in vars$covariates, avoiding treatment
contrasts that depend on the reference values of interacting covariates.
(#595)lifecycle package@return) of all exported
functions and marked internal helpers with
@keywords internal (#567).@examples to the exported user-facing
functions that were missing them, and marked the internal-only
has_class(), validate() and
longDataConstructor with @keywords internal
(#570).MNAR (rather than a mix of
NMAR and MNAR)nmar.rm argument of
longdata$get_data in favour of mnar.rm for
nomenclature consistency.imputation_list_single() where a failure of the
imputation-count-divisible-by-D check incorrectly reported
that “multiple ids were detected” (#574)mu$visit in
simulate_test_data() to refer to visit instead
of trt (#578)apply_delta() to correctly
refer to data instead of dat (#576)README.md (#548)structure() special name
.Dim with dim in unit tests to resolve an
R CMD check NOTE (#581)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 “proportional_em” or
“counterfactual” instead.analyse()
function (#370)rstan (#288)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.