Description: Provides R-implementation of Decision forest algorithm, which combines the predictions of
multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel
pattern-recognition method which can be used to analyze: (1) DNA microarray data;
(2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data; and
(3) Structure-Activity Relation (SAR) data.
In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.

Title: Finding Recurrent DNA Copy Number Alterations and Differences
Description: In tumor tissue, underlying genomic instability can lead to DNA copy number alterations,
e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the
genome, whereas recurrent alterations are observed in the same genomic region across multiple
independent samples, perhaps because they provide a selective growth advantage. Here we use
cyclic shift permutations to identify recurrent copy number alterations in a single cohort or
recurrent copy number differences in two cohorts based on a common set of genomic markers.
Additional functionality is provided to perform downstream analyses, including the creation of
summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al.
(2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al.
(2015) <doi:10.1093/biomet/asv046>. A manuscript based on DiNAMIC.Duo is currently under development.
Config/reticulate: list( packages = list( list(package = "numpy") ) )
Imports: biomaRt, dinamic, plyr, reticulate
Author: Vonn Walter [aut, cre] (<https://orcid.org/0000-0001-6114-6714>),
David Neil Hayes [aut] (<https://orcid.org/0000-0001-6203-7771>)

Title: Differential Pattern Analysis via Linear Modeling
Author: Ryan C. Sartor <sartorry@gmail.com>, Kathleen Greenham <greenham@umn.edu>
Maintainer: Ryan C. Sartor <sartorry@gmail.com>
Description: Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA' package). Using the framework of linear model-based differential expression comparisons ('limma' package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see Kathleen Greenham, Ryan C Sartor, Stevan Zorich, Ping Lou, Todd C Mockler and C. Robertson McClung (2020) <doi:10.1101/2020.04.03.024281>.