rIsing: High-Dimensional Ising Model Selection

Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the 'libLBFGS' optimization library by Naoaki Okazaki.

Author
Pratik Ramprasad [aut, cre], Jorge Nocedal [ctb, cph], Naoaki Okazaki [ctb, cph]
Date of publication
2016-11-25 08:43:07
Maintainer
Pratik Ramprasad <pratik.ramprasad@gmail.com>
License
GPL (>= 2)
Version
0.1.0

View on CRAN

Man pages

ising
High-Dimensional Ising Model Selection
logreg
L1 Regularized Logistic Regression
rIsing
rIsing: High-Dimensional Ising Model Selection.

Files in this package

rIsing
rIsing/src
rIsing/src/misc.h
rIsing/src/lbfgs
rIsing/src/lbfgs/arithmetic_sse_double.h
rIsing/src/lbfgs/arithmetic_ansi.h
rIsing/src/lbfgs/lbfgs.c
rIsing/src/lbfgs/arithmetic_sse_float.h
rIsing/src/lbfgs/lbfgs.h
rIsing/src/logreg.cpp
rIsing/src/RcppExports.cpp
rIsing/NAMESPACE
rIsing/R
rIsing/R/rIsing.R
rIsing/R/logreg.R
rIsing/R/RcppExports.R
rIsing/R/ising.R
rIsing/MD5
rIsing/DESCRIPTION
rIsing/man
rIsing/man/ising.Rd
rIsing/man/rIsing.Rd
rIsing/man/logreg.Rd