nbfar - Negative Binomial Factor Regression Models ('nbfar')
We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y. With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details.
Last updated 3 years ago
openblascpp
4.48 score 6 stars 6 scripts 170 downloadsrobregcc - Robust Regression with Compositional Covariates
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.
Last updated 4 years ago
openblascpp
4.11 score 6 stars 43 scripts 130 downloadsgofar - Generalized Co-Sparse Factor Regression
Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.
Last updated 3 years ago
openblascpp
3.00 score 2 stars 1 scripts 147 downloadssecure - Sequential Co-Sparse Factor Regression
Sequential factor extraction via co-sparse unit-rank estimation (SeCURE).
Last updated 4 years ago
openblascpp
1.04 score 11 scripts 169 downloads