XMRF

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

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 5 articles )

Showing results 1 to 5 of 5.
Sorted by year (citations)

  1. Ha, Min Jin; Stingo, Francesco Claudio; Baladandayuthapani, Veerabhadran: Bayesian structure learning in multilayered genomic networks (2021)
  2. Jonas M. B. Haslbeck, Lourens J. Waldorp: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2020) not zbMATH
  3. Park, Gunwoong; Park, Sion: High-dimensional Poisson structural equation model learning via (\ell_1)-regularized regression (2019)
  4. Sinclair, David; Hooker, Giles: Sparse inverse covariance estimation for high-throughput microRNA sequencing data in the Poisson log-normal graphical model (2019)
  5. Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)