fold 
Object specifying crossvalidation folds as generated by a call
to make_folds .

target 
The target (experimental) data set, in a standard format such
as a data.frame or matrix .

background 
The background data set, in a standard format such as a
data.frame or matrix . Note that the number of features must
match the number of features in the target data.

center 
A logical indicating whether the target and background
data sets should be centered to mean zero.

scale 
A logical indicating whether the target and background
data sets should be scaled to unit variance.

n_eigen 
A numeric indicating the number of eigenvectors (or
sparse contrastive components) to be computed. The default is to compute
two such eigenvectors.

alg 
A character indicating the SPCA algorithm used to sparsify
the contrastive loadings. Currently supports iterative for the
\insertRefzou2006sparsescPCA implemententation, var_proj for the
nonrandomized \insertReferichson2018sparsescPCA solution, and
rand_var_proj for the randomized
\insertReferichson2018sparsescPCA result.

contrasts 
A numeric vector of the contrastive parameters. Each
element must be a unique nonnegative real number. The default is to use 40
logarithmically spaced values between 0.1 and 1000.

penalties 
A numeric vector of the L1 penalty terms on the
loadings. The default is to use 20 equidistant values between 0.05 and 1.

clust_method 
A character specifying the clustering method to
use for choosing the optimal constrastive parameter. Currently, this is
limited to either kmeans, partitioning around medoids (PAM), and
hierarchical clustering. The default is kmeans clustering.

n_centers 
A numeric giving the number of centers to use in the
clustering algorithm. If set to 1, cPCA, as first proposed by Abid et al.,
is performed, regardless of what the penalties argument is set to.

max_iter 
A numeric giving the maximum number of iterations to
be used in kmeans clustering, defaulting to 10.

linkage_method 
A character specifying the agglomerative
linkage method to be used if clust_method = "hclust" . The options
are ward.D2 , single , complete , average ,
mcquitty , median , and centroid . The default is
complete .

n_medoids 
A numeric indicating the number of medoids to
consider if n_centers is set to 1. The default is 8 such medoids.

parallel 
A logical indicating whether to invoke parallel
processing via the BiocParallel infrastructure. The default is
FALSE for sequential evaluation.
