Penalized Poisson Pseudo Maximum Likelihood Regression

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) <> and takes advantage of the method of alternating projections of Gaure (2013) for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) 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) .


Reference manual

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0.1.0 by Nicolas Apfel, 15 days ago

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Authors: Diego Ferreras Garrucho [aut] , Tom Zylkin [aut] , Nicolas Apfel [cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rcpp, glmnet, lfe, ncvreg, tidyr, rlang, magrittr

Suggests testthat, MASS, knitr, rmarkdown, directlabels, ggplot2, reshape2

Linking to Rcpp, RcppEigen

See at CRAN