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  "Description": "Solves kernel ridge regression, within the the mixed model\nframework, for the linear, polynomial, Gaussian, Laplacian and\nANOVA kernels. The model components (i.e. fixed and random\neffects) and variance parameters are estimated using the\nexpectation-maximization (EM) algorithm. All the estimated\ncomponents and parameters, e.g. BLUP of dual variables and BLUP\nof random predictor effects for the linear kernel (also known\nas RR-BLUP), are available. The kernel ridge mixed model (KRMM)\nis described in Jacquin L, Cao T-V and Ahmadi N (2016) A\nUnified and Comprehensible View of Parametric and Kernel\nMethods for Genomic Prediction with Application to Rice. Front.\nGenet. 7:145. <doi:10.3389/fgene.2016.00145>.",
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