DPGP specifications

Information


Unique identifier OMICS_17942
Name DPGP
Alternative name Dirichlet process Gaussian process mixture model
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Stability Stable
Requirements GPy, Pandas, Numpy, Scipy, Matplotlib.pyplot
Maintained Yes

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Maintainer


  • person_outline Timothy Reddy <>

Publication for Dirichlet process Gaussian process mixture model

DPGP institution(s)
Computational Biology & Bioinformatics Graduate Program, Duke University, Durham, NC, USA; Center for Genomic & Computational Biology, Duke University, Durham, NC, USA; Department of Biostatistics & Bioinformatics, Duke University Medical Center, Durham, NC, USA; Biology Department, Duke University, Durham, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA; Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
DPGP funding source(s)
This work was funded by NIH R00 HG006265, NIH R01 MH101822, NIH U01 HG007900, a Sloan 605 Faculty Fellowship, NIH U01 HG007900, NIH F31 HL129743, CBB TG NIH 5T32GM071340, NSF 607 MCB 1417750.

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