Convenient Tool for Solving the Prize-Collecting Steiner Tree
Problem
The Prize-Collecting Steiner Tree problem asks to find a subgraph
connecting a given set of vertices with the most expensive nodes and least
expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm
does not exist. This package provides convenient functionality for obtaining an
approximate solution to this problem using loopy belief propagation algorithm.

This package provides a function simplifying presentation
of PCA models in a 3D interactive representation using rgl

Functions simplifying presentation of PCA models in a 3D interactive representation using 'rgl'.

This package provides a function simplifying presentation of PCA models in a 3D interactive representation using rgl

Create 2D Principal Component Plots with Bootstrapping
Draws a 2D principal component plot using the first 2 principal
components from the original and bootstrapped data to give some sense of
variability.

Sapan Jot <sapan.madaan@gmail.com> and Paul Brooks
<jpbrooks@vcu.edu>. The scripts configure and configure.ac and
the file INSTALL are based on those in R package clpAPI
<geliudie@uni-duesseldorf.de>.
This package contains three implementations of principal
component analysis methods using the L1 norm.  The package
depends on COIN-OR Clp version >= 1.12.0.  The methods
implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and Kanade 2003,
2005), and L1-PCA* (Brooks, Dula, and Boone 2012).

Sapan Jot <sapan.madaan@gmail.com> and Paul Brooks
<jpbrooks@vcu.edu>.

Sapan Jot <sapan.madaan@gmail.com> and Paul Brooks
<jpbrooks@vcu.edu>

Three implementations of principal component analysis methods
using the L1 norm.  The package depends on COIN-OR Clp version >=
1.12.0.  The methods implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and
Kanade 2003, 2005), and L1-PCA* (Brooks, Dula, and Boone 2013).

Sapan Jot <sapan.madaan@gmail.com>, Paul Brooks
<jpbrooks@vcu.edu>,
Andrea Visentin <andrea.visentin@insight-centre.org>,
and Young Woong Park <ywpark@mail.smu.edu>
Implementations of six methods for principal component analysis
using the L1 norm.  The package depends on COIN-OR Clp version >=
1.12.0.  The methods implemented are
PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>,
L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>,
L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>,
L1-PCAhp (Visentin, Prestwich and Armagan 2016)
<DOI:10.1007/978-3-319-46227-1_37>,
wPCA (Park and Klabjan 2016), and
awPCA (Park and Klabjan 2016).