NEWS	R Documentation

alpaca news

Changes in version 0.3.4

• Added vcov.APEs() generic to extract the covariance matrix after getAPEs().

• Improved the finite sample performance of bias corrections for the average partial effects in case of perfectly classified observations.

• Bias corrections for the average partial effects, i.e. getAPEs() after biasCorr(), do not require an offset algorithm anymore.

• The default option 'n.pop' in getAPEs() has been changed. Now the estimated covariance consists of the delta method part only, i.e. correction factor = 0.

• Improved the numerical stability of the bias corrections.

• biasCorr() now also supports one-way fixed effects models.

• Added bias corrections for 'cloglog' and 'cauchit'.

• feglm() and feglm.nb() do not return a matrix of scores anymore. Instead they, optionally, return the centered regressor matrix. The corresponding option in feglmControl() is 'keep.mx'. Default is TRUE.

• Improved the numerical stability of the step-halving in feglm().

• Changed the projection in the MAP algorithm.

• The default option 'center.tol' in feglmControl() has been lowered to better handle fitting problems that are not well-behaved.

• Added optional 'weights' argument to feglm() and feglm.nb().

• Updated documentation.

Changes in version 0.3.3

• Stopping condition of feglm.nb() has been adjusted to better match that of glm.nb().

• feglm.nb() now additionally returns 'iter.outer' and 'conv.iter' based on iterations of the outer loop. Previously 'iter' and 'conv' were overwritten.

• Step-halving in feglmFit() and feglmOffset() is now similar to glm.fit2().

• Fixed an error in the covariance (influence function) of getAPEs().

• Updated some references in the documentation and vignette.

• Fixed some typos in the documentation and vignette.

Changes in version 0.3.2

• Added option 'panel.structure' to biasCorr() and getAPEs(). This option allows to choose between the two-way bias correction suggested by Fernández-Val and Weidner (2016) and the bias corrections for network data suggested by Hinz, Stammann, and Wanner (2020). Currently both corrections are restricted to probit and logit models.

• Added option 'sampling.fe' to getAPEs() to impose simplifying assumptions when estimating the covariance matrix.

• feglm() now permits to expand functions with poly() and bs() (#9 @tcovert).

• feglm() now uses an acceleration scheme suggested by Correia, Guimaraes, and Zylkin (2019) that uses smarter starting values for centerVariables().

• Added an example of the three-way bias correction suggested by Hinz, Stammann, and Wanner (2020) to the vignette.

• The control parameter 'trace' now also returns the current parameter values as well as the residual deviance.

• Fixed an error in getAPEs() related to the estimation of the covariance.

• Fixed a bug in the internal function that is used to estimate spectral densities.

Changes in version 0.3.1

• All routines now use setDT() instead of as.data.table() to avoid unnecessary copies (suggested in #6 @zauster).

• feglm.nb() now returns 'iter' and 'conv' based on iterations of the outer loop.

• Fixed a bug in feglm() that prevented to use I() for the dependent variable.

• Fixed an error in getAPEs() related to the covariance.

• The last line of print.summary.feglm() now ends with a line break (#6 @zauster).

• The internal function feglmFit() now correctly sets 'conv' if the algorithm does not converge (#5 @zauster).

• Fixed some typos in the vignette.

Changes in version 0.3

• feglm() now allows to estimate binomial model with fractional response.

• Added feglm.nb() for negative binomial models.

• Added post-estimation routine biasCorr() for analytical bias-corrections (currently restricted to logit and probit models with two-way error component).

• Added post-estimation routine getAPEs() to estimate average partial effects and the corresponding standard errors (currently restricted to logit and probit models with two-way error component).

• getFEs() now returns a list of named vectors. Each vector refers to one fixed effects category (suggested in #4 @zauster).

• Changed stopping condition to the one used by glm().

• Changed least squares fit to QR (similar to lsfit() used by glm()).

• Source code and vignettes revised.

Changes in version 0.2

• Initial release on CRAN.