High dimensionality, noise and heterogeneity among
samples and features challenge the omic integration task. Here we
present an omic integration method based on sparse singular value
decomposition (SVD) to deal with these limitations, by: a. obtaining
the main axes of variation of the combined omics, b. imposing sparsity
constraints at both subjects (rows) and features (columns) levels
using Elastic Net type of shrinkage, and c. allowing both linear and
non-linear projections (via t-Stochastic Neighbor Embedding) of the
omic data to detect clusters in very convoluted data
(Gonzalez-Reymundez et. al, 2022) <doi:10.1093/bioinformatics/btac179>.
annotate, bigparallelr, bigstatsr, future.apply, scatterpie,
clValid, ComplexHeatmap, fpc, ggplot2, ggpmisc, ggthemes,
gridExtra, irlba, knitr, MASS, rmarkdown, testthat, viridis,
spelling, VennDiagram, grid

c(
person("Linda","Collins", email = "LMcollins@psu.edu", role = "aut"),
person("Liying","Huang", email = "lxh37@psu.edu", role = c("aut","cre")),
person("John","Dziak", email = "jjd264@psu.edu", role ="aut"))
Provides functions similar to the 'SAS' macros previously provided
to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
which outline practical benefits and challenges of factorial
and fractional factorial experiments for scientists interested
in developing biological and/or behavioral interventions, especially
in the context of the multiphase optimization strategy
(see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
currently contains three functions. First, RelativeCosts1() draws a graph
of the relative cost of complete and reduced factorial designs versus
other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
which contains a list of random numbers that can be used to conveniently
assign participants to conditions in an experiment with
many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
size, or required sample size of a factorial or fractional factorial
experiment, for main effects or interactions, given several possible choices
of effect size metric, and allowing pretests and clustering.
Linda Collins [aut],
Liying Huang [aut, cre],
John Dziak [aut]