Package: penppml
Title: Penalized Poisson Pseudo Maximum Likelihood Regression
Version: 0.1.0
Authors@R: c(
    person(given = "Diego",
           family = "Ferreras Garrucho",
           role = "aut",
           email = "d.ferreras-garrucho@lse.ac.uk"),
    person(given = "Tom",
           family = "Zylkin",
           role = "aut",
           email = "tzylkin@richmond.edu"),
    person(given = "Nicolas",
           family = "Apfel",
           role = "cre",
           email = "nicolas.apfel@gmail.com")   
    )       
Description: 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>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
LazyDataCompression: gzip
RoxygenNote: 7.1.1
LinkingTo: Rcpp, RcppEigen
Imports: Rcpp, glmnet, lfe, ncvreg, tidyr, rlang, magrittr
Depends: R (>= 2.10)
URL: https://github.com/tomzylkin/penppml
BugReports: https://github.com/tomzylkin/penppml/issues
Suggests: testthat (>= 3.0.0), MASS, knitr, rmarkdown, directlabels,
        ggplot2, reshape2
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2021-09-06 13:50:39 UTC; diego
Author: Diego Ferreras Garrucho [aut],
  Tom Zylkin [aut],
  Nicolas Apfel [cre]
Maintainer: Nicolas Apfel <nicolas.apfel@gmail.com>
Repository: CRAN
Date/Publication: 2021-09-09 05:30:02 UTC
