Package: dblr
Type: Package
Title: Discrete Boosting Logistic Regression
Version: 0.1.0
Author: Nailong Zhang
Maintainer: Nailong Zhang <setseed2016@gmail.com>
Description: Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single 
  logistic regression model, but not a sequence of logistic regression models. The fitted model
  object returned from the model training consists of two tables. One table is used to give the
  boundaries of bins for each continuous variable as well as the corresponding coefficients,
  and the other one is used for discrete variables. This package can also be used for binning
  continuous variables for other statistical analysis. 
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: data.table (>= 1.9.6), xgboost (>= 0.6-4), CatEncoders (>=
        0.1.1), Metrics (>= 0.1.1), methods
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-10-11 14:07:44 UTC; nl
Repository: CRAN
Date/Publication: 2017-10-11 17:31:59 UTC
Built: R 4.3.3; ; 2025-01-24 06:24:55 UTC; unix
