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GAGAs
View on CRAN: Click
here
Download and install GAGAs package within the R console
Install from CRAN:
install.packages("GAGAs")
Install from Github:
library("remotes")
install_github("cran/GAGAs") Install by package version:
library("remotes")
install_version("GAGAs", "0.6.2") Attach the package and use:
library("GAGAs")
Maintained by
Bin Wang
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-25
Latest Update: 2024-01-23
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) .
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).
How to cite:
Bin Wang (2022). GAGAs: Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models. R package version 0.6.2, https://cran.r-project.org/web/packages/GAGAs. Accessed 07 Mar. 2026.
Previous versions and publish date:
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Complete documentation for GAGAs
Functions, R codes and Examples using
the GAGAs R package
Some associated functions: GAGAs . LM_GAGA . cal.acc . cal.cindex . cal.w.acc . cox_GAGA . cpp_COX_gaga . cpp_logistic_gaga . cpp_multinomial_gaga . cpp_poisson_gaga . logistic_GAGA . multinomial_GAGA . poisson_GAGA . predict.GAGA . predict_LM_GAGA . predict_cox_GAGA . predict_logistic_GAGA . predict_multinomial_GAGA . predict_poisson_GAGA . rcpp_lm_gaga .
Some associated R codes: GAGAs.R . LM_GAGA.R . RcppExports.R . cal.acc.R . cal.cindex.R . cal.w.acc.R . cox_GAGA.R . logistic_GAGA.R . multinomial_GAGA.R . poisson_GAGA.R . predict.GAGA.R . Full GAGAs package functions and examples
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