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  "Title": "Poly-Omic Prediction of Complex TRaits",
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  "Date": "2016-03-03",
  "Author": "Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels,\nVassily Trubetskoy",
  "Maintainer": "Hae Kyung Im <haky@uchicago.edu>",
  "Description": "It provides functions to generate a correlation matrix\nfrom a genetic dataset and to use this matrix to predict the\nphenotype of an individual by using the phenotypes of the\nremaining individuals through kriging. Kriging is a\ngeostatistical method for optimal prediction or best unbiased\nlinear prediction. It consists of predicting the value of a\nvariable at an unobserved location as a weighted sum of the\nvariable at observed locations. Intuitively, it works as a\nreverse linear regression: instead of computing correlation\n(univariate regression coefficients are simply scaled\ncorrelation) between a dependent variable Y and independent\nvariables X, it uses known correlation between X and Y to\npredict Y.",
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  "Collate": "'correlation_matrices.R' 'input_pheno_GT.R' 'omic_KRIGR.R'",
  "Repository": "https://hakyimlab.r-universe.dev",
  "Date/Publication": "2020-10-27 14:35:35 UTC",
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      "topics": [
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    },
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      "title": "Run Principal Component Analysis (PCA) using base R svd() function.",
      "topics": [
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    },
    {
      "page": "okriging",
      "title": "Run omic kriging on a set of correlation matrices and a given phenotype.",
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    {
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      "title": "Read the GRM binary file.",
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