testthat 3rd
Edition. (0.2.10.2)plotmod(), labels regarding SDs
will no longer be displayed when
w_values is set. (0.2.10.1)Improved the printout of the summary()
of std_selected()
and std_selected_boot() outputs.
It now prints the R-squared increase
of the highest order term, as well as
the F test for the increase, if the
model has one and only one highest
order term (e.g., an interaction
term). (0.2.9.1)
Added the argument w_values to
cond_effect() and plolmod().
Users can specify the
values of the moderator (w)
to be used to compute the conditional
effects. (0.2.9.2)
update.std_selected(). Though
still not recommended, it should now
work more reliably if it needs to be
called. (0.2.9.1)stdmod-package. (0.2.8.9001)summary() of std_selected()
and std_selected_boot() outputs.
Small numbers are rounded to prevent
the use of scientific notation, and
small p-values can be printed in
formats like p<.001. Users can also
control the number of digits in the
printout. See the help page
of print.summary.std_selected()
to learn more about new arguments (0.2.8.9002).dplyr from the tests and Suggests. (0.2.7.2)visreg will be skipped if visreg is
not installed. (0.2.7.3)cond_effect-class
object and the summary of
a std_selected-class object. If one or more variables
are standardized but bootstrapping is not requested,
users will be recommended to use std_selected_boot().
(0.2.7.4)stdmod_lavaan() switched to the bootstrapping
algorithm used by lavaan(). It also updated to allow
for partial standardization. To use the older algorithm,
set use_old_version() to TRUE. (0.2.7.5)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().