print()
method of the summary() output of
std_selected(). (0.2.6.2)to_standardize to std_selected() and
std_selected_boot(). (0.2.6.3)confint.std_selected() when
type = "lm" and bootstrapping is requested. Should not be
an issue because t-based CIs should not be used when
bootstrapping is requested. This option is just for testing.
(0.2.6.4)to_standardize or mention it as
a shortcut. (0.2.6.5)to_standardize.
(0.2.6.6)summary() of
std_selected() and std_selected_boot()
outputs. (0.2.4.9001).ggplot2.
(0.2.4.9002)summary() of std_selected(). (0.2.4.9003)bibentry() in CITATION. (0.2.6)std_selected(): It now works correctly
when a variable in the data frame is a factor. (0.2.0.1)confint() and coef() methods for
cond_effect-class objects. confint() can
return confidence intervals based on t statistics, which are
appropriate in some situations. (0.2.2)print() method for cond_effect-class
objects can print confidence intervals based on t statistics.
(0.2.2)do_boot to std_selected_boot(). If
set to FALSE, it will not do bootstrapping. (0.2.3)cond_effect_boot() will disable bootstrapping in the
original call if the output is generated by
std_selected_boot(), to avoid redundant bootstrapping
inside bootstrapping. (0.2.3)do_boot to cond_effect_boot(). If
set to FALSE, it will not do bootstrapping. (0.2.4)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().