Description: Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the bigmatch package are optcal() to find the optimal caliper and nfmatch() to find a near-fine balance match with a caliper.

Description: Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors and nfmatch() to find a near-fine balance match with a caliper and a restriction on number of nearest neighbors.

Description: Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors.
Note: The optmatch package, which is useful for running many of the
provided functions, may be downloaded from Github at
on CRAN. The rrelaxiv package, which provides an alternative
solver for the underlying network flow problems, carries an
academic license and is not available on CRAN, but may be

Package: bigmatrix
Type: Package
Title: Tuning Insensitive Graph Estimation and Regression
Version: 0.9.1
Date: 2012-07-28
Author: Xingguo, Tuo Zhao, Lie Wang and Han Liu
Maintainer: Xingguo LI <xingguo.leo@gmail.com>
Depends: R (>= 2.14), glmnet, lattice, igraph, MASS, Matrix
Description: The package "bigmatrix" solves for the precision matrix
estimations with two different methods. The first is the tuning
insensitive graph estimation and regression implemented with
scaled Lasso. Another method is by estimating at a grid of
values for the constrained lambda and the augmented Lagrangian
multiplier rho based on constrained L1 minimization for sparse
precision matrix estimation, which guarantees positive definite
output by projected augmented Lagrangian method and further
accelerates the algorithm via coordinate descent and active set
methods.
License: GPL-2
Repository: CRAN
Packaged: 2012-07-28 21:14:28 UTC; admin-118
Date/Publication: 2012-07-29 06:21:29