as.data.frame method
(as.data.frame.cond_indirect_effects())
for cond_indirect_effects objects.
The print method (print.cond_indirect_effects())
now calls this method internally. (0.2.4.4)Though rare in real studies,
merge_mod_levels() was revised to
support merging wlevel objects
that have only one level in each of
them. (0.2.4.1)
When bootstrapping is done by lavaan,
only bootstrap estimates with no
errors will be kept (previously, they
will be set to NA). This also makes
the number of bootstrap samples reported
consistent with the number of successful
bootstrap replications. (0.2.4.2)
For functions such as cond_effects(),
if the model has only one outcome
variable (e.g., moderation only and
has no mediator), then the y argument
can be omitted. (0.2.4.6)
lavaan bootstrap estimates.
(0.2.4.2)Corrected the help page of
indirect_effects_from_list(). It
works on an object of the class
indirect_list, not of the class
cond_indirect_effects. (0.2.4.3)
Updated the vignettes (0.2.4.5).
Updated the internal lavaan.mi objects
used for tests. (0.2.4.7)
Add on.exit() to ensure that the clusters
created will be cleared if a function
is aborted (e.g., terminated by users).
(0.2.4.8)
Updated the way the expected CPU time
for bootstrapping, and the time is
displayed only if progress is
FALSE. (0.2.4.9)
plot() method of the output
of cond_indirect_effect() now supports
using facet_grid() to plot the
conditional effects when there are
two or more moderators in a path.
(0.2.3.3, 0.2.3.5, 0.2.3.6, 0.2.3.13)If all paths have the same x
variables, the argument x can be
omitted when calling
total_indirect_effect(). Similarly,
if all paths have the same y
variables, the argument y can also be
omitted when calling
total_indirect_effect(). (0.2.3.2)
If appropriate, most functions will
automatically convert a single
model fitted by lm() to a list by
lm2list(). This allows users to
use the output of lm() directly,
such as when computing effects in a
moderation-only model. (0.2.3.4)
P-values and confidence intervals based on standard errors will now be computed even for direct paths not moderated. (0.2.3.8)
Added print_all_cond_indirect_effects()
and print_all_cond_effects() for
printing the full output of all
conditional indirect effects.
(0.2.3.9)
Updated the print method of
indirect effect to print p-value
and confidence interval computed from
original standard error when
appropriate. (0.2.3.10)
Added for_each_path argument to the
print-method of indirect_list
objects. Setting it to TRUE to print
each path in the output of
many_indirect_effects() individually.
(0.2.3.12)
Added wrap_computation to the argument
of print.indirect() to wrap the printout
of long computational formulas. Default
set to TRUE.
(0.2.3.14)
Updated plot_effect_vs_w() to check
whether a moderator is numeric.
(0.2.3.15)
Updated print.cond_indirect_effects().
Disabled the printing of the (only)
component path when a path has no
mediator. (0.2.3.16)
Corrected README to remark that
dichotomous x-variables are supported
by most functions if coded as a
numeric dummy variable. (0.2.3.7)
Fix the wording in the printout
of lm_list objects. (0.2.3.11)
merge_model_matrix() in some special
cases. (0.2.3.1)Added support for using model-based
standard errors (and the covariances)
of estimates to compute the standard
errors, p-values, and confidence
intervals for conditional effects.
This should be used only for effects
without mediators and with both x-
and y-variables not standardized.
The standard errors, p-values,
and confidence intervals are printed
by default when certain conditions
are met. These statistics are for
models fitted by multiple regression.
They should be used with cautions for
models fitted by structural equation
modeling because the model may not be
invariant to linear transformation of
variables if some covariances involving
the product terms are not freely
estimated. (0.2.2.1)
Updated pseudo_johnson_neyman() to
support the SE-based test. The method
is equivalent to the (true)
Johnson-Neyman when SE-based test is
used. Added johnson_neyman() as an
alias to pseudo_johnson_neyman().
(0.2.2.4)
Added fill_wlevels() to add levels
of moderator to an output of
cond_indirect_effects(), such that
the confidence band of a plot of
effects against a moderator can be
smoother. (0.2.2.7)
mod_levels() in some special
cases. (0.2.2.5)Fixed a minor typo in the example
of indirect_effect(). (0.2.2.8)
Added cond_effects(), an alias to
cond_indirect_effects(). A better
name when a path has no mediator.
(0.2.2.9)
Finalize 0.2.3. Kept only updates relevant to users of 0.2.2. (0.2.3)
boot_type = "bc" in applicable
funcion. BC confidence interval is
not recommended in some recent works
(see Get Started
for selected references). It is added
merely for research purpose. (0.2.1.4)Try not setting the environment when
calling gen_boot_i_lavaan(), which
may not be necessary. (0.2.1.2)
Factored out confidence interval computation into an internal function. (0.2.1.3)
lm2list() now supports having only
one regression model as the input.
This allows users to use functions
such as cond_indirect_effects()
on a model with only moderators.
(0.2.1.5)
do_boot() for
multigroup models when all groups
have exactly the same number of
cases. (0.2.2.1)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)