{
    "identifier": "86C039BC7729668CE05324570681CDA42064",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Charles Camp Jr."
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/pages.nist.gov\/pyMCR",
    "title": "pyMCR: A Python Library for Multivariate Curve Resolution Analysis.",
    "description": "pyMCR is an open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of constraints, regressors, and error functions to minimize. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "downloadURL": "https:\/\/github.com\/usnistgov\/pyMCR",
            "format": "Python source code",
            "description": "pyMCR is a small package for performing multivariate curve resolution. Currently, it implements a simple alternating regression scheme (MCR-AR).",
            "mediaType": "text\/plain",
            "title": "pyMCR: Multivariate Curve Resolution for Python"
        },
        {
            "accessURL": "https:\/\/doi.org\/10.18434\/M32064",
            "title": "DOI Access for pyMCR: A Python Library for Multivariate Curve Resolution Analysis."
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2019-04-19",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Mathematics and Statistics:Numerical methods and software",
        "Mathematics and Statistics:Image and signal processing",
        "Chemistry:Analytical chemistry"
    ],
    "keyword": [
        "chemometrics",
        "endmember extraction",
        "multivariate curve resolution",
        "quantitative analysis",
        "spectral unmixing"
    ]
}