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().