Description: Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2016) <doi:10.1007/s00362-016-0865-5> and Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> for details.

Description: Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.

Description: Generalized factor model for ultra-high dimensional variables with mixed types.
We develop a two-step iterative procedure so that each update can be
carried out in parallel across all variables and samples. The fast
computation version is provided for ultra-high dimensional data,
see examples for more details. More details can be referred to
Packaged: 2022-01-04 06:20:59 UTC; Liuxianju

Title: Visualization technique for interpretation of high-throughput genetic or proteomic experiments.
Author: Dr. Sanjay Bhatikar <sanjay.bhatikar@biobase.in>, Kanika Arora <kanika.arora@biobase.
Description: GFMaps integrates datasets derived from gene
expression profiles with preexisting information from public
databases such as KEGG pathway or Gene Ontology using Gene Set
Enrichment Analysis (GSEA) and introduce a framework that allow
us to visualize biologically annotated datasets and relevant

Depends: affy, topGO, plotrix, KEGG.db, hgu95av2.db
Author: Dr. Sanjay Bhatikar <sanjay.bhatikar@biobase.in>, Kanika Arora <kanika.arora@biobase.in>

Title: Clustering and Inference Procedures for High-Dimensional Latent
Authors@R: person("Carson", "Eisenach", email = "eisenach@princeton.edu", role = c("aut", "cre"))
Description: 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 (2017) <arxiv:1806.00530>.