Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN, is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.
This is the R package `StructureMC' (Structured Matrix Completion) made and maintained by Yifu Liu(2012johnnyliu@gmail.com) and co-authored by Anru Zhang(anruzhang@stat.wisc.edu) and Tianxi Cai(tcai@hsph.harvard.edu).
You can install the StructureMC R package but before doing so user have to install devtools (via install.packages("devtools")), install the package using the following command:
library(devtools); # install_github('YifuLiu/StructureMC')
install.packages("StructureMC", repos = c('https://yifuliu.r-universe.dev', 'https://cloud.r-project.org'))
Additionally, windows users might encounter difficulty to install the 'StructureMC' package with only the above command. You might need to install the following command instead:
install.packages("devtools") ##If you have not install 'devtools' yet
## install 'curl' is the part windows users might need to implement
library(devtools); # install_github('YifuLiu/StructureMC')
install.packages("StructureMC", repos = c('https://yifuliu.r-universe.dev', 'https://cloud.r-project.org'))
For more information on this project please reference the paper "Cai, T., Cai, T. T., & Zhang, A. (2015). Structured Matrix Completion with Applications to Genomic Data Integration. Journal of the American Statistical Association, to appear."