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reglogit  

Simulation-Based Regularized Logistic Regression
View on CRAN: Click here


Download and install reglogit package within the R console
Install from CRAN:
install.packages("reglogit")

Install from Github:
library("remotes")
install_github("cran/reglogit")

Install by package version:
library("remotes")
install_version("reglogit", "1.2-7")



Attach the package and use:
library("reglogit")
Maintained by
Robert B. Gramacy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2011-08-07
Latest Update: 2023-04-25
Description:
Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 ).
How to cite:
Robert B. Gramacy (2011). reglogit: Simulation-Based Regularized Logistic Regression. R package version 1.2-7, https://cran.r-project.org/web/packages/reglogit. Accessed 06 Nov. 2024.
Previous versions and publish date:
1.0 (2011-08-07 10:08), 1.1-1 (2012-12-10 18:30), 1.1 (2012-01-04 08:31), 1.2-1 (2013-09-17 20:12), 1.2-2 (2014-01-15 17:49), 1.2-4 (2015-06-22 20:26), 1.2-5 (2017-11-19 19:22), 1.2-6 (2018-09-14 19:40), 1.2 (2013-04-24 08:14)
Other packages that cited reglogit R package
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Other R packages that reglogit depends, imports, suggests or enhances
Complete documentation for reglogit
Functions, R codes and Examples using the reglogit R package
Some associated functions: pima . predict.reglogit . reglogit-internal . reglogit . 
Some associated R codes: dRUM.R . reglogit.R .  Full reglogit package functions and examples
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