Releases: easystats/parameters
parameter 0.22.2
New supported models
- Support for models
glm_weightit
,multinom_weightit
andordinal_weightit
from package WeightIt.
Changes
-
Added
p_significance()
methods for frequentist models. -
Methods for
degrees_of_freedom()
have been removed.degrees_of_freedom()
now callsinsight::get_df()
. -
model_parameters()
for data frames anddraws
objects from package
posterior also gets anexponentiate
argument.
Bug fixes
- Fixed issue with warning for spuriously high coefficients for Stan-models
(non-Gaussian).
parameters 0.22.1
Breaking changes
- Revised calculation of the second generation p-value (SGPV) in
equivalence_test()
,
which should now be more accurate related to the proportion of the interval
that falls inside the ROPE. Formerly, the confidence interval was simply treated
as uniformly distributed when calculating the SGPV, now the interval is assumed
to be normally distributed.
New supported models
- Support for
svy2lme
models from package svylme.
Changes
standardize_parameters()
now also prettifies labels of factors.
Bug fixes
-
Fixed issue with
equivalence_test()
when ROPE range was not symmetrically
centered around zero (e.g.,range = c(-99, 0.1)
). -
model_parameters()
foranova()
from mixed models now also includes the
denominator degrees of freedom in the output (df_error
). -
print(..., pretty_names = "labels")
for tobit-models from package AER now
include value labels, if available. -
Patch release, to ensure that performance runs with older version of datawizard
on Mac OS X with R (old-release).
parameters 0.22.0
Breaking changes
-
Deprecated arguments in
model_parameters()
forhtest
,aov
and
BFBayesFactor
objects were removed. -
Argument
effectsize_type
is deprecated. Please usees_type
now. This change
was necessary to avoid conflicts with partial matching of argument names (here:
effects
).
New supported models
-
Support for objects from
stats::Box.test()
. -
Support for
glmgee
models from package glmtoolbox.
Bug fix
-
Fixed edge case in
predict()
forfactor_analysis()
. -
Fixed wrong ORCID in
DESCRIPTION
.
parameters 0.21.7
Changes
- Fixed issues related to latest release from marginaleffects.
Bug fixes
-
Fixes issue in
compare_parameters()
for models from package blme. -
Fixed conflict in
model_parameters()
when bothinclude_reference = TRUE
and
pretty_names = "labels"
were used. Now, pretty labels are correctly updated
and preserved.
parameters 0.21.6
New supported models
- Support for models of class
serp
(serp).
Changes
-
include_reference
can now directly be set toTRUE
inmodel_parameters()
and doesn't require a call toprint()
anymore. -
compare_parameters()
gains ainclude_reference
argument, to add the
reference category of categorical predictors to the parameters table. -
print_md()
forcompare_parameters()
now by default uses the tinytable
package to create markdown tables. This allows better control for column
heading spanning over multiple columns.
Bug fixes
-
Fixed issue with parameter names for
model_parameters()
and objects from
package epiR. -
Fixed issue with
exponentiate = TRUE
formodel_parameters()
with models
of classclmm
(package ordinal), when model had nocomponent
column
(e.g., no scale or location parameters were returned). -
include_reference
now also works when factor were created "on-the-fly" inside
the model formula (i.e.y ~ as.factor(x)
).
parameters 0.21.5
Bug fixes
- Fixes CRAN check errors related to the changes in the latest update of
marginaleffects.
parameters 0.21.4
Breaking changes
- The
exponentiate
argument ofmodel_parameters()
for
marginaleffects::predictions()
now defaults toFALSE
, in line with all
the othermodel_parameters()
methods.
Changes
-
model_parameters()
for models of package survey now gives informative
messages whenbootstrap = TRUE
(which is currently not supported). -
n_factors()
now also returns the explained variance for the number of
factors as attributes. -
model_parameters()
for objects of package metafor now warns when unsupported
arguments (likevcov
) are used. -
Improved documentation for
pool_parameters()
.
Bug fixes
-
print(include_reference = TRUE)
formodel_parameters()
did not work when
run inside a pipe-chain. -
Fixed issues with
format()
for objects returned bycompare_parameters()
that included mixed models.
parameters 0.21.3
Changes
-
principal_components()
andfactor_analysis()
now also work when argument
n = 1
. -
print_md()
forcompare_parameters()
now gains more arguments, similar to
theprint()
method. -
bootstrap_parameters()
andmodel_parameters()
now accept bootstrapped
samples returned bybootstrap_model()
. -
The
print()
method formodel_parameters()
now also yields a warning for
models with logit-links when possible issues with (quasi) complete separation
occur.
Bug fixes
-
Fixed issue in
print_html()
for objects from package ggeffects. -
Fixed issues for
nnet::multinom()
with wide-format response variables (using
cbind()
). -
Minor fixes for
print_html()
method formodel_parameters()
. -
Robust standard errors (argument
vcov
) now works forplm
models.
parameters 0.21.2
Changes
-
Minor improvements to factor analysis functions.
-
The
ci_digits
argument of theprint()
method formodel_parameters()
now
defaults to the same value ofdigits
. -
model_parameters()
for objects from package marginaleffects now also
accepts theexponentiate
argument. -
The
print()
,print_html()
,print_md()
andformat()
methods for
model_parameters()
get aninclude_reference
argument, to add the reference
category of categorical predictors to the parameters table.
Bug fixes
-
Fixed issue with wrong calculation of test-statistic and p-values in
model_parameters()
forfixest
models. -
Fixed issue with wrong column header for
glm
models with
family = binomial("identiy")
. -
Minor fixes for
dominance_analysis()
.
parameters 0.21.1
General
- Added support for models of class
nestedLogit
(nestedLogit).
Changes to functions
-
model_parameters()
now also prints correct "pretty names" when predictors
where converted to ordered factors inside formulas, e.g.y ~ as.ordered(x)
. -
model_parameters()
now prints a message when thevcov
argument is provided
andci_method
is explicitly set to"profile"
. Else, whenvcov
is not
NULL
andci_method
isNULL
, it defaults to"wald"
, to return confidence
intervals based on robust standard errors.