Changes in Version 3.1-0
o The betatree() function now uses the new mob() implementation from the
"partykit" package (instead of the old "party" package). The user interface
essentially remained the same but now many more options are available through
the new mob() function. The returned model object is now inheriting from
"modelparty"/"party".
o Included "grDevices" in Imports.
o Fixed model.frame() method for "betareg" objects which do not store the
model frame in $model.
o betamix() gained arguments "weights" (case weights for observations) and
"offset" (for the mean linear predictor).
Changes in Version 3.0-5
o The "Formula" package is now only in Imports but not Depends (see below).
o Method "FLXgetModelmatrix" for "FLXMRbeta" objects modified due to
changes in flexmix 2.3.12.
Changes in Version 3.0-4
o For some datasets betareg() would just "hang" because dbeta() "hangs"
for certain extreme parameter combinations (in current R versions).
betareg() now tries to catches these cases in order to avoid the problem.
o Depends/Imports/Suggests have been rearranged to conform with current
CRAN check policies. This is the last version of "betareg" to have the
"Formula" package in Depends - from the next version onwards it will
only be in Imports.
Changes in Version 3.0-3
o The predict() method gained support for type = "quantile", so that
quantiles of the response distribution can be predicted.
o The "Formula" package is now not only in the list of dependencies
but is also imported in the NAMESPACE, in order to facilitate
importing "betareg" in other packages.
Changes in Version 3.0-2
o Avoid .Call()ing logit link functions directly, instead
use elements of make.link("logit").
Changes in Version 3.0-1
o Small consistency updates in labeling coefficients for
current R-devel.
Changes in Version 3.0-0
o New release accompanying the second JSS paper: "Extended Beta
Regression in R: Shaken, Stirred, Mixed, and Partitioned" by Gruen,
Kosmidis, and Zeileis which appears as Journal of Statistical
Software 48(11). See also citation("betareg"). The paper presents
the recently introduced features: bias correction/reduction in
betareg(), recursive partitioning via betatree(), and finite
mixture modeling via betamix(). See also vignette("betareg-ext",
package = "betareg") for the vignette version within the package.
Changes in Version 2.4-1
o Formula interface for betamix() changed to allow for three parts
in the right hand side where the third part relates to the
concomitant variables.
o Modified the internal structure of vignettes/tests. The original
vignettes are now moved to the vignettes directory, containing also
.Rout.save files. Similarly, an .Rout.save for the examples is
added in the tests directory.
Changes in Version 2.4-0
o Support bias-corrected (BC) and bias-reduced (BR) maximum likelihood
estimation of beta regressions. See the "type" argument of betareg().
To enable BC/BR, an additional Fisher scoring iteration was added
that (by default) also enhances the usual ML results.
o New vignette("betareg-ext", package = "betareg") introducing BC/BR
estimation along with the recent additions beta regression trees and latent
class beta regression (aka finite mixture beta regression models).
o Enabled fitting of beta regression models without coefficients in the
mean equation.
o Enabled usage of offsets in both parts of the model, i.e., one can use
betareg(y ~ x + offset(o1) | z + offset(o2)) which is also equivalent to
betareg(y ~ x | z + offset(o2), offset = o1), i.e., the "offset"
argument of betareg is employed for the mean equation only. Consequently,
betareg_object$offset is now a list with two elements (mean/precision).
o Added warning and ad-hoc workaround in the starting value selection
of betareg.fit() for the precision model. If no valid starting value can be
obtained, a warning is issued and c(1, 0, ..., 0) is employed.
o Added betareg_object$nobs in the return object containing the number
of observations with non-zero weights. Then nobs() can be used to extract
this and consequently BIC() can be used to compute the BIC.
Changes in Version 2.3-0
o New betatree() function for beta regression trees based
on model-based recursive partitioning. betatree() leverages
the mob() function from the "party" package. For enabling this
plug-in, a "StatModel" constructor betaReg() is provided
based on the "modeltools" package.
o New betamix() function for latent class beta regression, or
finite mixture beta regression models. betamix() leverages the
flexmix() function from the "flexmix" package. For enabling this
plug-in, the driver FLXMRbeta() is provided.
o Added tests/vignette-betareg.R based on the models fitted
in vignette("betareg", package = "betareg").
Changes in Version 2.2-3
o The "levels" element of a "betareg" object is now a list
with components "mean", "precision", and "full" to match
the "terms" of the object.
o Improved data handling bug in predict() method.
Changes in Version 2.2-2
o Documentation updates for ?gleverage.
Changes in Version 2.2-1
o Package now published in Journal of Statistical Software,
see http://www.jstatsoft.org/v34/i02/
and citation("betareg") within R.
o Bug fix and improvements in gleverage() method for "betareg"
objects: Analytic second derivatives are now used and
variable dispersion models are handled correctly.
Changes in Version 2.2-0
o dbeta(..., log = TRUE) is now used for computing the
log-likelihood which is numerically more stable
than the previous hand-crafted version.
o The starting values in the dispersion regression are
now chosen differently, resulting in a somewhat more
robust specification of starting values. The intercept
is computed as described in Ferrari & Cribari-Neto
(2004), plus a link transformation (if any). All further
parameters (if any) are initially set to zero. See also
the vignette for details.
o Various documentation improvements, especially in the
vignette.
Changes in Version 2.1-2
o New vignette (written by Francisco Cribari-Neto and Z)
introducing the package and replicating a range of
publications related to beta regression:
vignette("betareg", package = "betareg")
provides some theoretical background, a discussion of the
implementation and several hands-on examples.
o Implemented an optional precision model, yielding
variable dispersion. The precision parameter phi may
depend on a linear predictor, as suggested by
Simas, Barreto-Souza, and Rocha (2010). In single part
formulas of type y ~ x1 + x2, phi is by default assumed to
be constant, i.e., an intercept plus identity link. But
it can be extended to y ~ x1 + x2 | z1 + z2 where phi
depends on z1 + z2, by default through a log link.
o Allowed all link functions (in mean model) that are
available in make.link() for binary responses, and added
log-log link.
o Added data and replication code for Smithson & Verkuilen
(2006, Psychological Methods). See ?ReadingSkills,
?MockJurors, ?StressAnxiety as well as the complete
replication code in demo("SmithsonVerkuilen2006").
o Default in residuals() (as well as in the related plot()
and summary() components) is now to use standardized
weighted residuals 2 (type = "sweighted2").
Changes in Version 2.0-0
o Package "betareg" was orphaned on CRAN, Z took over
as maintainer, ended up re-writing the whole package.
The package still provides all functionality as before
but the interface is not fully backward-compatible.
o betareg(): more standard formula-interface arguments;
"betareg" objects do _not_ inherit from "lm" anymore.
o betareg.fit(): renamed from br.fit(), enhanced interface
with more arguments and returned information. Untested
support of weighted regressions is enabled.
o betareg.control(): new function encapsulating control
of optim(), slightly modified default values.
o anova() method was removed, use lrtest() from "lmtest"
package instead.
o gen.lev.betareg() was changed to gleverage() method
(with new generic) and a bug in the method was fixed.
o envelope.beta() was removed and is now included in
plot() method for "betareg" objects.
o Datasets "prater" and "pratergrouped" were incorporated
into a single "GasolineYield" dataset.
o New data set "FoodExpenditure" from Griffiths et al. (1993),
replicating second application from Ferrari and Cribari-Neto
(2004).
o Added NAMESPACE.
o The residuals() method now has three further types of
residuals suggested by Espinheira et al. (2008) who recommend
to use "standardized weighted residuals 2" (type = "sweighted2").
The default are Pearson (aka standardized) residuals.