EPGLM: Gaussian Approximation of Bayesian Binary Regression Models

The main functions compute the expectation propagation approximation of a Bayesian probit/logit models with Gaussian prior. More information can be found in Chopin and Ridgway (2015). More models and priors should follow.

Author
James Ridgway
Date of publication
2016-08-23 16:31:11
Maintainer
James Ridgway <james.ridgway@ensae.fr>
License
GPL (>= 2)
Version
1.1.2

View on CRAN

Man pages

EPGLM-package
The package computes a Gaussian approximation of a Bayesian...
EPlogit
Compute the EP (expectation propagation) approximation of a...
EPlogitCxx
C++ internal function to compute the EP approximation (use...
EPprobit
Compute the EP (expectation propagation) approximation of a...
EPprobitCxx
C++ internal function to compute the EP approximation (use...

Files in this package

EPGLM
EPGLM/src
EPGLM/src/header.h
EPGLM/src/Makevars
EPGLM/src/EP.h
EPGLM/src/EPclogit.h
EPGLM/src/function.cpp
EPGLM/NAMESPACE
EPGLM/R
EPGLM/R/EP.R
EPGLM/MD5
EPGLM/DESCRIPTION
EPGLM/man
EPGLM/man/EPprobit.Rd
EPGLM/man/EPlogitCxx.Rd
EPGLM/man/EPGLM-package.Rd
EPGLM/man/EPprobitCxx.Rd
EPGLM/man/EPlogit.Rd