Nothing
bracl
objects with non-identifiable parameters.Work on output consistently from print()
methods for summary.XYZ
objects; estimator type is now printed and other fixes.
Enriched warning when algorithm does not converge with more informative text.
Documentation fixes and updates
brnb()
allows fitting negative binomial regression models using
implicit and explicit bias reduction methods. See vignettes for a
case study.simulate()
method for objects of class brmultinom
and bracl
ordinal_superiority()
method to estimate Agresti and Kateri
(2017)'s ordinal superiority measures, and compute bias corrections
for those.Wald.ratios = TRUE
in
summary.brmultinom
.vcov.bracl
that would return an error if the
"bracl"
object was computed using bracl()
with parallel = TRUE
and one covariate.bracl()
related to the handling or zero weights
that could result in hard-to-traceback errors.bracl()
that could cause errors in fits with one
covariate.brglmFit()
iteration returns last estimates that worked if
iteration fails.confint()
was not returning anything when applied
to objects of class brmultinom
.control
glm()
.
argument was specified using the output from brglmControl()
or
brglm_control()
.check_aliasing
option in brglmControl()
to tell
brglm_fit()
to skip (check_aliasing = TRUE
) or not
(check_aliasing = FALSE
) rank deficiency checks (through a QR
decomposition of the model matrix), saving some computational effort.NA
coefficients when brglmFit()
was
called with a vector x
or an x
with no column names.confint
method for brmulitnom
objectsvcov.brglmFit()
now uses vcov.summary.glm()
and supports the
complete
argument for controlling whether the variance covariance
matrix should include rows and columns for aliased parameters.detect_sepration()
and check_infinite_estimates()
, which
will be removed from brglm2 at version 0.8. New versions of
detect_sepration()
and check_infinite_estimates()
are now maintained
in the
detectseparation
R package.print.summary()
for brmultinom
and
bracl
objects.detect_separation()
now handles one-column model matrices correctly.brglmFit()
can now do maximum penalized likelihood with powers of
the Jeffreys prior as penalty (type = "MPL_Jeffreys
) for all
supported generalized linear models. See the help files of
brglmControl()
and brglmFit()
for details.?brglmFit
.print.brmultinom()
is now exported, so bracl
and brmultinom
objects print correctly.response_adjustment
argument in brglmControl()
to allow
for more fine-tuning of the starting values when brglmFit()
is
called with start = NULL
.brglmControl()
.brglmFit()
now works as expected with custom link functions (mean
and median bias reduction).brglmFit()
respects the specification of the transformation
argument in brglmControl()
.brglmFit()
.quasi()
, quasibinomial()
and
quasibinomial()
families and documentation update.bracl()
for fitting adjacent category logit models for ordinal
responses using maximum likelihood, mean bias reduction, and median
bias reduction and associated methods (logLik
, summary
and so
on).predict()
methods for brmultinom
and bracl
objects.
Added residuals()
methods for brmultinom
and bracl
objects
(residuals of the equivalent Poisson log-linear model)mis()
link functions for accounting for
misclassification in binomial response models (Neuhaus, 1999,
Biometrika).summary()
method for brmultinom
objects.NA
dispersion for models with 0
df
resid.type = AS_mixed
as an option to use mean-bias reducing
score functions for the regression parameters and median-bias
reducing score functions for the dispersion in models with unknown
dispersion.check_infinite_estimates()
now accepts brmultinom
objects.singular.ok
argument to brglmFit()
and
detect_separation()
methods in line with the update of
glm.fit()
.brglm_control()
.brglmControl()
is now exported.slowit
did nothing; now included in iteration.detect_separation()
method for the glm()
function can be used
to check for separation in binomial response settings without
fitting the model. This relies on a port of Kjell Konis'
safeBinaryRegression:::separator()
function (see ?detect_separation).type = "AS_median"
.brglmFit()
, brglm_fit()
, detectSeparation()
,
detect_separation()
, brglm_control()
, brglmControl()
,
detectSeparationControl()
, detect_separation_control()
,
checkInfiniteEstimates()
, check_infinite_estimates()
).cho2inv()
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