gforce.FDR_control

\(d x d\) symmetric matrix of test statistics.

test_stats

alpha level for the FDR control procedure.

alpha

a string. <code>procedure == 'BY'</code> indicates to use the Benjamini-Yekutieli thresholding rule.
<code>procedure == 'BH'</code> indicates to use the Benjamini-Hochberg thresholding rule.

procedure

Performs by default the Benjamini-Yekutieli FDR control procedure. Optionally, the Benjamini-Hochberg thresholding
rule can be used instead. As input it takes a symmetric matrix of test statistics with standard normal null
distributions. The number of hypotheses tested is \(d(d-1)/2\).

A complete suite of computationally efficient methods for high dimensional clustering and inference problems in G-Latent Models (a type of Latent Variable Gaussian graphical model). The main feature is the FORCE (First-Order, Certifiable, Efficient) clustering algorithm which is a fast solver for a semi-definite programming (SDP) relaxation of the K-means problem. For certain types of graphical models (G-Latent Models), with high probability the algorithm not only finds the optimal clustering, but produces a certificate of having done so. This certificate, however, is model independent and so can also be used to certify data clustering problems. The 'GFORCE' package also contains implementations of inferential procedures for G-Latent graphical models using n-fold cross validation. Also included are native code implementations of other popular clustering methods such as Lloyd's algorithm with kmeans++ initialization and complete linkage hierarchical clustering. The FORCE method is due to Eisenach and Liu (2019) <arxiv:1806.00530>.

gforce.FDR_control: FDR Control Procedure.

Description

Usage

Arguments

Value

References