Package: bigmatrix
Type: Package
Title: Tuning Insensitive Graph Estimation and Regression
Version: 0.9.5
Date: 2012-09-22
Author: Xingguo Li, 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" estimates high dimensional
precision matrices for Gaussian graphical models. We adopt two
estimation procedures based column by column regression: (1)
Tuning-Insensitive Graph Estimation and Regression (TIGER). (2)
The Constrained L1 Minimization for Sparse Precision Matrix
Estimation (CLIME). (3) Adaptive Lasso for Sparse Precision
Matrix Estimation (ADP). The optimization algorithm for CLIME
and ADP are based on alternative direction method combined with
coordinate decent. The computation is memory-optimized using
the sparse matrix output.
License: GPL-2
Repository: CRAN
Packaged: 2012-09-22 22:12:09 UTC; admin-118
Date/Publication: 2012-09-23 06:12:32

Package: bigmatrix
Type: Package
Title: Tuning Insensitive Graph Estimation and Regression
Version: 0.9.6
Date: 2012-10-16
Author: Xingguo Li, 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" estimates high dimensional
precision matrices for Gaussian graphical models. We adopt
three estimation procedures based on column by column
regression: (1) Tuning-Insensitive Graph Estimation and
Regression (TIGER); (2) The Constrained L1 Minimization for
Sparse Precision Matrix Estimation (CLIME); (3) Adaptive CLIME
for Sparse Precision Matrix Estimation (ADP). The optimization
algorithms for all three methods are based on alternative
direction method combined with coordinate decent. The
computation is memory-optimized using the sparse matrix output.
License: GPL-2
Repository: CRAN
Packaged: 2012-10-16 19:21:59 UTC; admin-118
Date/Publication: 2012-10-17 07:26:07

Title: Multidimensional Scaling for Big Data
Description: We present a set of algorithms for Multidimensional Scaling (MDS) to be used with large datasets.
MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n.
When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained.
With this package, we address these problems by means of three algorithms: Divide and Conquer MDS, Fast MDS and
MDS based on Gower interpolation. The main idea of these methods is based on partitioning the dataset into small
pieces, where classical methods can work.
Packaged: 2021-01-14 17:39:05 UTC; cristianpachongarcia
Author: Cristian Pachón García [aut, cre]
Maintainer: Cristian Pachón García <cc.pachon@gmail.com>