Movatterモバイル変換

NEWS	R Documentation

alpaca news

Addedvcov.APEs() generic to extract the covariance matrix aftergetAPEs().
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() afterbiasCorr(), do not require an offset algorithm anymore.
The default option 'n.pop' ingetAPEs() 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() andfeglm.nb() do not return a matrix of scores anymore. Instead they, optionally, return the centered regressor matrix. The corresponding option infeglmControl() is 'keep.mx'. Default is TRUE.
Improved the numerical stability of the step-halving infeglm().
Changed the projection in the MAP algorithm.
The default option 'center.tol' infeglmControl() has been lowered to better handle fitting problems that are not well-behaved.
Added optional 'weights' argument tofeglm() andfeglm.nb().
Updated documentation.

Stopping condition offeglm.nb() has been adjusted to better match that ofglm.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 infeglmFit() andfeglmOffset() is now similar toglm.fit2().
Fixed an error in the covariance (influence function) ofgetAPEs().
Updated some references in the documentation and vignette.
Fixed some typos in the documentation and vignette.

Added option 'panel.structure' tobiasCorr() andgetAPEs(). 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' togetAPEs() to impose simplifying assumptions when estimating the covariance matrix.
feglm() now permits to expand functions withpoly() andbs() (#9 @tcovert).
feglm() now uses an acceleration scheme suggested by Correia, Guimaraes, and Zylkin (2019) that uses smarter starting values forcenterVariables().
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 ingetAPEs() related to the estimation of the covariance.
Fixed a bug in the internal function that is used to estimate spectral densities.

All routines now usesetDT() instead ofas.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 infeglm() that prevented to useI() for the dependent variable.
Fixed an error ingetAPEs() related to the covariance.
The last line ofprint.summary.feglm() now ends with a line break (#6 @zauster).
The internal functionfeglmFit() now correctly sets 'conv' if the algorithm does not converge (#5 @zauster).
Fixed some typos in the vignette.

feglm() now allows to estimate binomial model with fractional response.
Addedfeglm.nb() for negative binomial models.
Added post-estimation routinebiasCorr() for analytical bias-corrections (currently restricted to logit and probit models with two-way error component).
Added post-estimation routinegetAPEs() 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 byglm().
Changed least squares fit to QR (similar tolsfit() used byglm()).
Source code and vignettes revised.