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