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.

AuthorPratik Ramprasad [aut, cre], Jorge Nocedal [ctb, cph], Naoaki Okazaki [ctb, cph]
Date of publication2016-11-25 08:43:07
MaintainerPratik Ramprasad <pratik.ramprasad@gmail.com>
LicenseGPL (>= 2)
Version0.1.0

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Files

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

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