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A set of tools that enables efficient estimation of penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models that feature one or more sets of high-dimensional fixed effects. The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> and takes advantage of the method of alternating projections of Gaure (2013) <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) <doi:10.1080/07350015.2015.1102733>.
| Version: | 0.2.3 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp, glmnet, fixest, collapse, rlang, magrittr, matrixStats, dplyr, devtools |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | testthat (≥ 3.0.0), MASS, knitr, rmarkdown, ggplot2, reshape2 |
| Published: | 2023-09-08 |
| DOI: | 10.32614/CRAN.package.penppml |
| Author: | Diego Ferreras Garrucho [aut], Tom Zylkin [aut], Joao Cruz [cre], Nicolas Apfel [ctb] |
| Maintainer: | Joao Cruz <jm01780 at surrey.ac.uk> |
| BugReports: | https://github.com/tomzylkin/penppml/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/tomzylkin/penppml |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| In views: | Econometrics |
| CRAN checks: | penppml results |
| Reference manual: | penppml.pdf |
| Vignettes: |
Penalized PPML Regression with penppml |
| Package source: | penppml_0.2.3.tar.gz |
| Windows binaries: | r-devel: penppml_0.2.3.zip, r-release: penppml_0.2.3.zip, r-oldrel: penppml_0.2.3.zip |
| macOS binaries: | r-release (arm64): penppml_0.2.3.tgz, r-oldrel (arm64): penppml_0.2.3.tgz, r-release (x86_64): penppml_0.2.3.tgz, r-oldrel (x86_64): penppml_0.2.3.tgz |
| Old sources: | penppml archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.