{ "_id": "6a216472cd65a98ecbd34271", "Package": "rchemo", "Type": "Package", "Title": "Dimension Reduction, Regression and Discrimination for\nChemometrics", "Version": "0.1-4", "Authors@R": "c(\nperson(\"Marion\", \"Brandolini-Bunlon\", email = \"marion.brandolini-bunlon@inrae.fr\", role = c(\"aut\", \"cre\")),\nperson(\"Benoit\", \"Jaillais\", email = \"benoit.jaillais@inrae.fr\", role = c(\"aut\")),\nperson(\"Jean-Michel\", \"Roger\", email = \"jean-michel.roger@inrae.fr\", role = c(\"aut\")),\nperson(\"Matthieu\", \"Lesnoff\", email = \"matthieu.lesnoff@cirad.fr\", role = c(\"aut\")))", "Description": "Data exploration and prediction with focus on high\ndimensional data and chemometrics. The package was initially\ndesigned about partial least squares regression and\ndiscrimination models and variants, in particular locally\nweighted PLS models (LWPLS). Then, it has been expanded to many\nother methods for analyzing high dimensional data. The name\n'rchemo' comes from the fact that the package is orientated to\nchemometrics, but most of the provided methods are fully\ngeneric to other domains. Functions such as transform(),\npredict(), coef() and summary() are available. Tuning the\npredictive models is facilitated by generic functions\ngridscore() (validation dataset) and gridcv()\n(cross-validation). 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