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>.

Robust penalized (adaptive) elastic net S and M estimators for
    linear regression. The methods are proposed in
    Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
    (2019) <https://projecteuclid.org/euclid.aoas/1574910036>.
    The package implements the extensions and algorithms described in
    Kepplinger, D. (2020) <doi:10.14288/1.0392915>.

Simulation of continuous, correlated high-dimensional data with 
    time to event or binary response, and parallelized functions for Lasso, 
    Ridge, and Elastic Net penalized regression with repeated starts and 
    two-dimensional tuning of the Elastic Net.

Estimate penalized synthetic control models
    and perform hold-out validation to determine their 
    penalty parameter. This method is based on the work by
    Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>.
    Penalized synthetic controls smoothly interpolate between 
    one-to-one matching and the synthetic control method.

Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".

Data sets for statistical inference modeling related to People Analytics.  
  Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' 
  by Keith McNulty (2020).