In get_add() and get_drop(), the
representation will switch to RAM
if LISREL fails.
(0.3.0.1)
If the option modelbpp.do_fit is set
to FALSE or not set, a parameter table will not
be fitted to get the model df, leading
to faster search. If this options is
set to TRUE, then the
parameter table will be fitted as
in 0.3.0.2 or older version.
(0.3.0.2, 0.3.0.3)
Disabled more options to make the search faster. (0.3.0.3)
Depends on R 4.1.0 or later now. (0.3.0.2)
Added the argument
exclude_x_changed_to_y to model_set()
and friends, as well as
get_add(). Though this is rarely
desirable, setting this argument to
FALSE allow changes that make a
"pure" x-variable a y-variable.
(0.2.0.1)
Added drop_equivalent_models to
gen_models(). (0.2.0.2)
Updated print.model_set() to handle
sem_out and function that are not
names. (0.2.0.4)
gen_models() now correctly handles
cross_add = NULL. (0.2.0.3)
identical_partables() now correctly
ignores ustart for free parameters.
(0.2.0.5)
Breaking Changes: Added the arguments
cross_add and
cross_sets to model_set() and
get_add() to better handle factor
loadings. Some factor loadings are no longer
added by default, such as an indicator
loading on two latent factors and
one of the factors regresses on
another. These cross-loadings are not
meaningful in some models and may lead
to nonconvergence.
To reproduce results in previous
versions of modelbpp, users may need
to manually add these loadings using
cross_add and cross_sets. (0.1.6.3)
Breaking Changes: Added the argument
loadings_to_exclude_from_drop
to model_set() (and loadings_to_exclude
to get_add()) to better handle factor
loadings. Some factor loadings will no longer
be considered to be dropped by default,
such as an indicator
that loads on only one latent factor.
Cross-loadings will still be considered.
To reproduce results in previous
versions of modelbpp, users may need
to set loadings_to_exclude_from_drop
to "none". (0.1.6.3)
Added a few more tests (0.1.6.2)
Updated gen_models() with the new
arguments added to model_set().
(0.1.6.4)
add_list_duplicate_cov(), which
makes must_not_add failed to exclude
some covariances. It should work now.
(0.1.6.1)model_set_combined() for
computing BPPs for models from two or
more calls to model_set().
(0.1.5.3, 0.1.5.4)The default of
more_fit_measures of the print
method of model_set-class object
was changed to c("cfi", "rmsea", "srmr).
(0.1.5.1)
Changed the vignettes to precomputed Rmarkdown files. (0.1.5.2)
Added the argument exclude_xy_cov
and exclude_feedback to get_add()
and model_set(), for excluding paths
that create feedback loops, and
covariances involving a predictor
and an outcome variable (including
those linked by indirect paths).
Default values has been changed
to TRUE since 0.1.3.5. To
reproduce results from previous version,
set them to FALSE. (0.1.3.2, 0.1.3.5)
Added min_bpp_labelled to
model_graph(), to hide the labels
of models with small BPPs.
(0.1.3.5)
Added the argument drop_equivalent_models,
to model_set(). If TRUE, the
default, the models fitted will be
checked for equivalence. If two or
more more models are equivalent, only
one of them will be retained.
The groups of equivalent models identified,
and the models dropped, will be
printed by the print method. (0.1.3.9)
Added measurement_invariance_models(),
for generating metric and scalar
invariance models and their partial
invariance versions. (0.1.3.10 - 0.1.3.11)
Because it is very likely that users
would like to see come fit measures
along with BPPs, the default of
more_fit_measures of the print
method of model_set-class object
changed to c("cfi", "rmsea").
(0.1.3.7)
Revised fit_many() to support
multigroup models. (0.1.3.8)
A progress bar can be displayed when
model_set() is identifying nested
models. (0.1.3.13)
Shortened BIC Posterior Probability
to BPP in some sections of the
printout of print.model_set().
(0.1.3.14)
Cumulative BPPs no longer displayed
by default in print.model_set().
Print them by setting cumulative_bpp
to TRUE. (0.1.3.15)
Update an internal function to handle nonconvergence in checking nested relation. Only affect the graphs and only happen in some rare cases. (0.1.3.16)
The must_not_add argument should
work now for some parameters that may
not be recognized as interchangeable.
(0.1.3.1)
Fixed a bug in must_not_drop and
must_drop of get_drop(). They
should work properly now. (0.1.3.5)
Fixed a bug in model_graph().
Short names should now be properly
constructed. (0.1.3.3)
Fixed some bugs in print.model_set()
about the printing of additional fit
measures. (0.1.3.6, 0.1.3.7)
Fixed a bug in checking whether two models are equivalent. (0.1.3.12)
model_set()model_set() to work with
user-supplied models. These models
are supplied as parameter tables
through the argument partables.
(0.1.2.7)print-method of
model_set-class objects. Users can
set the prior probabilities of one or
more models of their choice. (0.1.2.7)c-method for partables-class
and model_set-class objects. For the
ease of adding user models when calling
model_set(). (0.1.2.7)lavaan-class objects)
to model_set() through the
argument sem_out. (0.1.2.17)print method of model_set()
supports printing additional fit
measures available from
lavaan::fitMeasures(). Check the
argument more_fit_measures.
(0.1.2.27)print method of model_set()
support printing short model names,
which can be used to interpret
the output of model_graph().
(0.1.2.29, 0.1.2.30)model_graph() to determine
nested relation using the method
by Bentler and Satorra (2010). This
can be done only if fixed.x
is set to FALSE. (0.1.2.10, 0.1.2.19)model_graph() if nested relations
need to be determined. (0.1.2.28)model_graph()model_graph() to plot
user-supplied models. (0.1.2.7)model_graph() with new options.
If drop_redundant_direct_paths
is TRUE (default), redundant
direct paths will be removed.model_graph().
(0.1.2.20)model_graph()
to label arrows by model df
differences (see label_arrow_by_df),
and weight arrow widths by
model df differences (see
weight_arrows_by_df). (0.1.2.20)partables-class object. (0.1.2.8)partables-class objects. (0.1.2.9, 0.1.2.12)model_graph()
to use short names in the
graph, if they are created and
stored by model_set().
(0.1.2.29, 0.1.2.30)lavaan::modificationIndices() about
equality constraints. (0.1.2.5)unique_models() to handle
user-supplied models. (0.1.2.6)model_set(). (0.1.2.11)print
method of partables. (0.1.2.13)model_set() will check whether
the sum of user-supplied prior
probabilities is less than 1. (0.1.2.14)print method of
model_set objects to print original
model dfs. (0.1.2.15)fit_many(), can set the
model with whichfit_many() will compute
model df difference. (0.1.2.16)model_set-class object to print
models that failed the past.check
of lavaan. (0.1.2.32)model_set() if no paths are dropped
or no paths are added. (0.1.2.3)gen_models()'s argument, output.tinytest for tests. (0.1.0.9004 - 0.1.0.9006)