Package: GAGAs
Type: Package
Title: Global Adaptive Generative Adjustment Algorithm for Generalized
        Linear Models
Version: 0.6.1
Language: en-US
Authors@R: c(
      person("Bin", "Wang", role=c("aut", "cre"), email="eatingbeen@hotmail.com"),
      person("Xiaofei", "Wang", role=c("ctb")),
      person("Jianhua", "Guo", role=c("ths"))
      )
Description: Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm.  
 For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. 
 This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. 
 Further study is going on for the nonorthogonal cases and generalized linear models. 
 These works are in part supported by the National Natural Foundation of China (No.12171076). 
License: GPL-2
URL: https://arxiv.org/abs/1911.00658
Encoding: UTF-8
Depends: R (>= 3.6.0)
Imports: Rcpp (>= 1.0.9), survival, utils
Suggests: mvtnorm
SystemRequirements: C++17
LinkingTo: Rcpp, RcppEigen
RoxygenNote: 7.2.3
Maintainer: Bin Wang <eatingbeen@hotmail.com>
NeedsCompilation: yes
Packaged: 2023-03-30 00:02:35 UTC; Administrator
Author: Bin Wang [aut, cre],
  Xiaofei Wang [ctb],
  Jianhua Guo [ths]
Repository: CRAN
Date/Publication: 2023-03-30 02:20:02 UTC
Built: R 4.2.0; x86_64-apple-darwin17.0; 2023-03-30 12:32:05 UTC; unix
Archs: GAGAs.so.dSYM
