Rcpp (>= 0.12.10), mclust, gtools, blockmodels

person("Scott", "Miller", email = "sam3CRAN@gmail.com",
role = c("aut", "cre"))
utils, stats, graphics, plyr, orthopolynom, polynom, glmnet
Generates feature matrix outputs from R object inputs
using a variety of expansion functions.  The generated
feature matrices have applications as inputs
for a variety of machine learning algorithms.
The expansion functions are based on coercing the input
to a matrix, treating the columns as features and
converting individual columns or combinations into blocks of
columns.
Currently these include expansion of columns by
efficient sparse embedding by vectors of lags,
quadratic expansion into squares and unique products,
powers by vectors of degree,
vectors of orthogonal polynomials functions,
and block random affine projection transformations (RAPTs).
The transformations are
magrittr- and cbind-friendly, and can be used in a
building block fashion.  For instance, taking the cos() of
the output of the RAPT transformation generates a
stationary kernel expansion via Bochner's theorem, and this
expansion can then be cbind-ed with other features.
Additionally, there are utilities for replacing features,
removing rows with NAs,
creating matrix samples of a given distribution,
a simple wrapper for LASSO with CV,
a Freeman-Tukey transform,
generalizations of the outer function,
matrix size-preserving discrete difference by row,
plotting, etc.

Expanding Ploidy and Allele Frequency on Nested Subpopulations (ExPANdS) characterizes coexisting subpopulations (SPs) in a tumor using copy number and allele frequencies derived from exome- or whole genome sequencing input data. The model amplifies the statistical power to detect coexisting genotypes, by fully exploiting run-specific tradeoffs between depth of coverage and breadth of coverage. ExPANdS predicts the number of clonal expansions, the size of the resulting SPs in the tumor bulk, the mutations specific to each SP and tumor purity. The main function runExPANdS provides the complete functionality needed to predict coexisting SPs from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of the subpopulation predictions by ExPANdS increases with the number of mutations provided. It is recommended that at least 200 mutations are used as an input to obtain stable results.