Simulate Omics-Scale Data with Dependency

Documentation for package ‘dependentsimr’ version 1.0.0.0

draw_from_multivariate_corr	Draw random samples from the given random structure
get_random_structure	Compute structure of dependency from a given data
match_with_spiked_wishart	Compute what spiked SD values will give you the desired top eigenvalues by iteratively solving
multi_sample_spiked_wishart	Compute means of each singular value and the mean Jacobian, see sample_spiked_wishart_and_jac
read_counts	GSE151923: cortex from 6-month-old wildtype C57BL/6 mice
remove_dependence	Remove all dependence in a random structure
sample_spiked_wishart	Efficiently sample the singular values corresponding to a random Wishart matrix with spiked eigenvalues Specifically, if W = G G^T with each column of G drawn iid from N(0, Sigma), then W is a Wishart matrix and this function samples the singular values of G. The eigenvalues of W are just the squares of the singular values. Here, Sigma is diagonal with its leading entries from spiked_sd^2 and all remaining entries are population_sd^2.
sample_spiked_wishart_and_jac	Efficiently sample the singular values corresponding to a random Wishart matrix with spiked eigenvalues and the Jacobian I.e., these are the singular values of G if GG^T is Wishart. The square of these give the eigenvalues of the random Wishart matrix.