High-Performance Open-Source Archive
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().
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