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.