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.
