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.