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  "Title": "Solvers for Large-Scale Eigenvalue and SVD Problems",
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  "Authors@R": "c(\nperson(\"Yixuan\", \"Qiu\", , \"yixuan.qiu@cos.name\", c(\"aut\", \"cre\")),\nperson(\"Jiali\", \"Mei\", , \"vermouthmjl@gmail.com\", \"aut\",\ncomment = \"Function interface of matrix operation\"),\nperson(\"Gael\", \"Guennebaud\", , \"gael.guennebaud@inria.fr\", \"ctb\",\ncomment = \"Eigenvalue solvers from the 'Eigen' library\"),\nperson(\"Jitse\", \"Niesen\", , \"jitse@maths.leeds.ac.uk\", \"ctb\",\ncomment = \"Eigenvalue solvers from the 'Eigen' library\")\n)",
  "Description": "R interface to the 'Spectra' library\n<https://spectralib.org/> for large-scale eigenvalue and SVD\nproblems. It is typically used to compute a few\neigenvalues/vectors of an n by n matrix, e.g., the k largest\neigenvalues, which is usually more efficient than eigen() if k\n<< n. This package provides the 'eigs()' function that does the\nsimilar job as in 'Matlab', 'Octave', 'Python SciPy' and\n'Julia'. It also provides the 'svds()' function to calculate\nthe largest k singular values and corresponding singular\nvectors of a real matrix. The matrix to be computed on can be\ndense, sparse, or in the form of an operator defined by the\nuser.",
  "License": "MPL (>= 2)",
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      "title": "Find a Specified Number of Eigenvalues/vectors of a Square Matrix",
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        "eigs.dgeMatrix",
        "eigs.dgRMatrix",
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    {
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        "svds.dgCMatrix",
        "svds.dgeMatrix",
        "svds.dgRMatrix",
        "svds.dsCMatrix",
        "svds.dsRMatrix",
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