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Support for mediation paths in multigroup models fitted by lavaan has been added. Demonstrations can be found in this article
Many functions have been updated to work for multigroup models with mediators fitted by lavaan. Most common tasks support multigroup models. For functions that support moderators, the group variable will be used automatically as a moderator. Checks will be added to functions not yet support multigroup models to alert users.
For paths moderated in multigroup models, only some functions (e.g., cond_indirect_effect()) are supported. However, multigroup models with moderators are rare. Functions that do not yet support multigroup models (e.g, mod_levels()) will raise an error if used on a multigroup model. Support may be added in the future.
The + and - operators can now be used on different paths because they may be paths in different groups in multigroup models.
The plot-method of cond_indirect_effects-class objects will be forced to be a tumble graph if the lines for different groups are to be plotted. In these cases, the data within each group will be used, including standardization. This approach, though leading to results different from those in single-group model using the group as a moderator, makes more sense for multigroup models, in which the distribution of variables are allowed to be different between groups. Since 0.1.14.10, by default, the model implied statistics are used to determine the means and SDs used in drawing the plot. This approach is useful when between-group equality constraints are present.
The plot-method of cond_indirect_effects-class objects now supports plotting a path that involves latent variables. The model implied statistics will always be used for the latent variables when determining the means and SDs. This is useful because the group-variable can be treated as a moderator by cond_indirect_effects(). (0.1.14.7)
Added plot_effect_vs_w(). It can plot an effect (direct or indirect) against a moderator, using the output of cond_indirect_effects(). (0.1.14.14 - 0.1.14.15)
Added pseudo_johnson_neyman(). It used the pseudo Johnson-Neyman approach (Hayes, 2022) to find the value of a moderator at which the conditional effect (direct or indirect) changes from nonsignificant to significant (or vice versa), based on the confidence interval selected. (0.1.14.16)
If a dataset has a variable which is a product of itself and another variable (e.g., x*y == x), find_products() will be trapped in an infinite loop. This “product term” will no longer be treated as a “product term.” (0.1.14.1)
Bootstrapping and Monte Carlo simulation will no longer be run once for each path in many_indirect_effects(). If do_boot() or do_mc() is not used first but bootstrapping or Monte Carlo confidence intervals are requested, this process will be done only once, and the estimates will be reused by all paths. (0.1.14.9, a bug fixed in 0.2.1)
scale_x and scale_y) for each bootstrap or simulated sample are now stored, such that the confidence interval of the unstandardized effect can be computed even if standardization is requested. (0.1.13.2)indirect_raw, though not used for now, is now computed correctly when using + and -. (0.1.13.1)delta_med-class object. (0.1.13.4)do_mc() on a model which do not have a mean structure, has latent variables, and is estimated by multiple imputation. Error is no longer raised. (0.1.13.5)delta_med() for computing \(\Delta_{Med}\) (Delta_Med), an \(R^2\)-like measure of indirect effect proposed by Liu, Yuan, and Li (2023). Can form nonparametric bootstrap confidence interval for \(\Delta_{Med}\). (0.1.12.1, 0.1.12.3)se = TRUE). They are simply the standard deviations of the bootstrap estimates (if bootstrap confidence intervals are requested) or simulated estimates (if Monte Carlo confidence intervals are requested). They should be interpreted with cautions because the sampling distribution of the effect estimates may not be symmetric. (0.1.11.2)Customized linters. (0.1.11.1)
Revised a test to accommodate a behavior of MKL when MASS::mvrnorm() is used to generate pseudo random numbers. (0.1.11.4)
Finalized to 0.1.12. (0.1.12)
P-value were not computed when mathematical operations are conducted on effects using + and - before version 0.1.11.2. This has been fixed. (0.1.11.2)
merge_model_matrix() failed if all variables in an lm() output is already present in merged outputs. Fixed in 0.1.11.3. (0.1.11.3)
cond_indirect() did not hide the progress when Monte Carlo CIs were requested and do_mc() was called internally. Fixed. It now hides the progress if progress = TRUE. (0.1.11.5)
runMI() or sem.mi() from the semTools package using multiple imputation. (0.1.9.8-0.1.9.10)indirect_proportion() and two methods for its output. (0.1.9.12)get_prod() and added an article on its workflow. (0.1.9.13).fixed.x argument as lavaan does. (0.1.9.17)factor2var() to work (again) for a categorical variable with only two levels. (0.1.9.21)pkgdown site. (0.1.9.2)pkgdown site. (0.1.9.6)do_mc(). (0.1.9.11)print.mc_out(), the print-method for mc_out-class objects. (0.1.9.14)pkgdown GitHub action for using newer version of mermaid. (0.1.9.15)pkgdown website to use the new logo and color scheme. (0.1.9.16)lavaan on handling random seed. (0.1.9.18)pkgdown articles, accessible through the pkgdown website of the package. (0.1.9.19)lavaan.mi-class objects. (0.1.9.20)lavaan on handling random seed.lm2boot_out_parallel() to do bootstrapping with the output of lm() using parallel processing. This is the default when do_boot() is used on the outputs of lm(). (0.1.4.4)do_boot(). (0.1.4.7)all_indirect_paths() for identifying all indirect paths in a model. (0.1.4.5)many_indirect_effects() for computing indirect effects for a list of paths. (0.1.4.5)total_indirect_effect() for computing the total indirect effect between two variables. (0.1.4.5)expect_equal on numbers rather than on characters. No change in the functions. (0.1.4.3)merge_model_frame(). (0.1.4.8)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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