std_selected(): It now works correctly
when a variable in the data frame is a factor. (0.2.0.1)confint() and
vcov() for std_selected-class object. If
bootstrap CIs are requested, then bootstrap CIs and VCOV based on
bootstrapping should be returned. (0.2.0.0)(All major changes after 0.1.7.1)
plotmod(). It now correctly handles more
than two levels when w_method is set
to"percentile". (0.1.7.2, 0.1.7.3)(All major changes after 0.1.5)
plotmod() for plotting moderation effects. This
function will check whether a variable is standardized. If yes, will
note this in the plot.plotmod() can also plot a Tumble graph (Bodner, 2016)
if graph_type is set to "tumble".plotmod() instead of
visreg::visreg().cond_effect() for computing conditional effects.
This function will check which variable(s) is/are standardized. If yes,
will note this in the printout.cond_effect_boot(), a wrapper of
cond_effect() that can form nonparametric bootstrap
confidence intervals for the conditional effects, which may be partially
or completely standardized.std_selected() and std_selected_boot().stdmod_lavaan() now returns an object of the class
stdmod_lavaan, with methods print, confint, and coef
added.std_selected_boot() output. Bootstrap confidence intervals
are placed next to parameter estimates.vcov() method for std_selected()
output. If bootstrapping is used, it can return the variance-covariance
matrix of the bootstrap estimates.confint() method for std_selected()
output. If bootstrapping is used, it can return the bootstrap percentile
confidence intervals if requested.std_selected().