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reglogit
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]
[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:
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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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