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  "Title": "Mean-Variance Regularization",
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  "Date": "2018-09-10",
  "Authors@R": "c(person(\"Jean-Eudes\", \"Dazard\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jean-eudes.dazard@case.edu\"),\nperson(\"Hua\", \"Xu\",\nrole = \"ctb\",\nemail = \"huaxu77@gmail.com\"),\nperson(\"Alberto\", \"Santana\",\nrole = \"ctb\",\nemail = \"ahs4@case.edu\"))",
  "Author": "Jean-Eudes Dazard [aut, cre], Hua Xu [ctb], Alberto Santana\n[ctb]",
  "Maintainer": "Jean-Eudes Dazard <jean-eudes.dazard@case.edu>",
  "Description": "This is a non-parametric method for joint adaptive\nmean-variance regularization and variance stabilization of\nhigh-dimensional data. It is suited for handling difficult\nproblems posed by high-dimensional multivariate datasets (p >>\nn paradigm). Among those are that the variance is often a\nfunction of the mean, variable-specific estimators of variances\nare not reliable, and tests statistics have low powers due to a\nlack of degrees of freedom. Key features include: (i)\nNormalization and/or variance stabilization of the data, (ii)\nComputation of mean-variance-regularized t-statistics\n(F-statistics to follow), (iii) Generation of diverse\ndiagnostic plots, (iv) Computationally efficient implementation\nusing C/C++ interfacing and an option for parallel computing to\nenjoy a faster and easier experience in the R environment.",
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  "URL": "https://github.com/jedazard/MVR",
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