The 'Vowpal Wabbit' project is a fast out-of-core learning
system sponsored by Microsoft Research (having started at Yahoo! Research)
and written by John Langford along with a number of contributors. This R
package does not include the distributed computing implementation of the
cluster/ directory of the upstream sources. Use of the software as a network
service is also not directly supported as the aim is a simpler direct call
from R for validation and comparison.

Alexandre Bureau, Ingo Ruczinski, Samuel G. Younkin
Computes estimates of the probability of related individuals sharing a rare variant.
AllGenerics.R AllClasses.R functions.R methods-pedigree.R
methods-trio.R approx2_functions.R compute_kinship_prop_coef.R
ped2trio.R PFU.R rare_variant_sharing_v3.R zzz.R

AllGenerics.R AllClasses.R methods-pedigree.R methods-trio.R
methods-RVsharingProb.R functions.R approx2_functions.R
ped2trio.R PFU.R rare_variant_sharing_v3.R zzz.R

Probability of Sharing Rare Variants among Relatives

AllGenerics.R AllClasses.R methods-pedigree.R methods-trio.R
methods-RVsharingProb.R functions.R approx2_functions.R
ped2trio.R PFU.R rare_variant_sharing_v3.R extract_carriers.R
pvalue_functions.R RVgene.R zzz.R

AllGenerics.R AllClasses.R methods-pedigree.R methods-trio.R
methods-RVsharingProb.R functions.R approx2_functions.R
ped2trio.R PFU.R RVsharing.fn.R extract_carriers.R
pvalue_functions.R RVgene.R zzz.R

AllGenerics.R AllClasses.R methods-pedigree.R methods-trio.R
methods-RVsharingProb.R functions.R approx2_functions.R
ped2trio.R PFU.R RVsharing.fn.R extract_carriers.R
pvalue_functions.R RVgene.R RVgene_allshare.R zzz.R

Rare Variant Tests Using Multiple Regression Methods
Regularized multiple regression methods: ridge regression
(RR), principal components regression (PCR), partial least
squares regression (PLS), sparse PLS (SPLS), and the LASSO for
testing rare variants association with disease traits.

Use multiple regression methods to test rare variants
association with disease traits.

Detects spatial outliers using a Random Walk on Bipartite Graph
a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.