Added xlimits and ylimits arguments to the
g_mmrm_lsmeans function.
scda and scda.2022 in
vignettes with random.cdisc.data.Adapt to release 0.3 of the mmrm package.
... to
mmrm::mmrm when calling fit_mmrm. In
particular, the method argument allows to choose
Kenward-Roger adjustment of degrees of freedom and coefficients
covariance matrix.optimizer argument when
calling fit_mmrm.parallelly is now used internally to handle the
determination of available cores.mmrm package instead of lme4 and
lmerTest. This greatly increases convergence and speed.
Different covariance structures and optimizers are now available
compared to before.g_covariance() to visualize a covariance
matrix, which can be helpful for choosing or visualizing the covariance
structure in the MMRM.averages_emmeans to
fit_mmrm() which allows estimation of least square means
for averages of visits.accept_singular to fit_mmrm()
which allows estimation of rank-deficient models (like lm()
and gls()) by omitting the columns of singular coefficients
from the design matrix.show_lines and xlab to
g_mmrm_lsmeans() which allow the addition of lines
connecting the estimates, as well as a custom x-axis label.table_stats, table_formats,
table_labels, table_font_size, and
table_rel_height to g_mmrm_lsmeans() which
allow the addition of and configure the appearance of an LS means
estimates statistics table below the LS means estimates plot.constant_baseline and
n_baseline to g_mmrm_lsmeans() which allow
plotting of a constant baseline value and specifying the corresponding
number of patients (visible in the optional table) for the LS means
plots.purrr, tibble,
scda and scda.2022mmrm_test_data as
sample data.tern.