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. |