README.md

In GFORCE: Clustering and Inference Procedures for High-Dimensional Latent Variable Models

GFORCE: An R package for high-dimensional clustering and inference in cluster-based graphical models

Author: Carson Eisenach

Please send all correspondence to eisenach [AT] princeton.edu.

Summary

This is the current development version of the GFORCE package.

This package provides implementations of state-of-the-art clustering algorithms and inference procedures introduced in - Eisenach, C. and Liu, H. (2017). Efficient, Certifiably Optimal High-Dimensional Clustering. arXiv:1806.00530. - Eisenach, C., Bunea, F., Ning, Y. and Dinicu, C. (2018). Efficient, High-Dimensional Inference for Cluster-Based Graphical Models. Manuscript submitted for publication.

The new methods implemented include: - FORCE - a fast solver for a semi-definite programming (SDP) relaxation of the K-means problem. For certain data generating distributions it produces a certificate of optimality with high probability, and - Inferential procedures and FDR control for cluster based graphical models.

Also provided are high quality implementations of traditional clustering algorithms: - Lloyd's algorithm, - kmeans++ initializations, - hierarchical clustering

GFORCE documentation built on May 2, 2019, 3:44 a.m.