The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Minor update: Use new Stan array syntax thanks to Andrew Johnson.
Minor fix: Let rstantools generate Makevars
Minor housekeeping:
Updated compiler flags for new version of RStan thanks to Andrew Johnson.
Fix package for staged installation
tab2doc()
, package no longer needs archived
ReporteRs package.tab2doc()
because required package ReporteRs
is archived.mlm_spaghetti_plot()
to allow jittering
and adjusting size of the error bars.mlm_spaghetti_plot()
now has argument mx
which can be set to mx = "data"
to plot the spaghetti plot
of the M - Y relationship (b path) such that the X values are from data,
and not fitted values from the X - M model (a path). The argument
defaults to mx = "fitted"
, such that the X axis values of
the M - Y spaghetti plot are fitted values.mlm_spaghetti_plot()
for visualizing
model-fitted values for paths a (X->M regression) and b (M->Y
regression)mlm_summary()
now gives only population level
parameters by default, and group-level parameters when
pars = "random"
mlm_path_plot()
now draws a template if no model is
entered (i.e. template
argument is deprecated)mlm_path_plot()
now by default also shows SDs of
group-level effects. This behavior can be turned off by specifying
random = FALSE
MEC2010
mlm_summary()
Removed sigma_y from being modeled when binary_y = TRUE.
Removed posterior probabilities from default outputs.
Added type = “violin” as option for plotting coefficients with mlm_pars_plot().
Users may now change each individual regression parameter’s prior, instead of classes of priors.
Users may now change the shape parameter of the LKJ prior.
Coefficient plots now reorder parameter estimates, if user has requested varying effects.
Path plot now by default does not scale the edges.
bmlm now uses pre-compiled C++ code for the Stan models, which
eliminates the need to compile a model each time mlm()
is
run. This significantly speeds up model estimation.
The Stan code used by mlm()
is now built from separate
chunks, allowing more flexible and robust model development.
Initial release to CRAN.
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.
Health stats visible at Monitor.