MALLARD specifications

Information


Unique identifier OMICS_29152
Name MALLARD
Alternative name MultinomiAL Logistic-normAl dynamic lineaR moDels
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python, R
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Justin Silverman <>
  • person_outline Heather Durand <>
  • person_outline Rachael Bloom <>
  • person_outline Sayan Mukherjee <>
  • person_outline Lawrence David <>

Publication for MultinomiAL Logistic-normAl dynamic lineaR moDels

MALLARD institution(s)
Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA; Medical Scientist Training Program, Duke University, Durham, NC, USA; Center for Genomic and Computational Biology, Duke University, Durham, NC, USA; University Program for Genetics and Genomics, Duke University, Durham, NC, USA; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA; Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, NC, USA
MALLARD funding source(s)
Supported in part by the Duke University Medical Scientist Training Program (GM007171), the Global Probiotics Council, a Searle Scholars Award, the Hartwell Foundation, an Alfred P. Sloan Research Fellowship, NIH 1R01DK116187-01 and grants NSF IIS-1546331, NSF DMS-1418261, NSF IIS-1320357, NSF DMS-1045153, and NSF DMS1613261.

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