R package XMRF: Markov Random Fields for High-Throughput Genetics Data. Fit Markov Networks to a wide range of high-throughput genomics data.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Ha, Min Jin; Stingo, Francesco Claudio; Baladandayuthapani, Veerabhadran: Bayesian structure learning in multilayered genomic networks (2021)
- Jonas M. B. Haslbeck, Lourens J. Waldorp: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2020) not zbMATH
- Park, Gunwoong; Park, Sion: High-dimensional Poisson structural equation model learning via (\ell_1)-regularized regression (2019)
- Sinclair, David; Hooker, Giles: Sparse inverse covariance estimation for high-throughput microRNA sequencing data in the Poisson log-normal graphical model (2019)
- Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)