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"https://github.com/famuvie/breedR/raw/HEAD/README.md", "_rundeps": [ "boot", "cli", "cpp11", "farver", "ggplot2", "glue", "gtable", "HaploSim", "isoband", "labeling", "lattice", "lifecycle", "lme4", "MASS", "Matrix", "minqa", "nlme", "nloptr", "pedigree", "pedigreemm", "plyr", "R6", "rbibutils", "RColorBrewer", "Rcpp", "RcppEigen", "Rdpack", "reformulas", "reshape", "rlang", "S7", "scales", "sp", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": "Additive-Genetic-Models-in-Mixed-Populations.Rmd", "filename": "Additive-Genetic-Models-in-Mixed-Populations.pdf", "title": "Additive Genetic Models in Mixed Populations", "author": "Facundo Muñoz", "engine": "knitr::rmarkdown", "headings": [ "Method 1: Hybrids as an independent population", "Method 2: GCA/SCA model for hybrids", "Method 3: grid search" ], "created": "2015-05-03 13:03:19", "modified": "2020-06-10 07:13:23", "commits": 5 }, { "source": "Overview.Rmd", "filename": "Overview.pdf", "title": "BreedR Overview", "author": "Facundo Muñoz", "engine": "knitr::rmarkdown", "headings": [ "Intro", "What is breedR", "Installation", "Where to find help", "License", "Roadmap | Future developments", "Functionality", "Inference", "Frequentist", "Bayesian", "Linear Mixed Models with unstructured random effects", "Example dataset", "A simple Provenance Test", "Initial variances specification", "Exploring the results", "Further extractor functions", "Hierarchical and Factorial models", "Model specification", "Interactions", "Exercise | Hierarchical and Factorial models", "Hierarchical and Factorial models #1 | Fitting models", "Hierarchical and Factorial models #2 | Hierarchical model", "Hierarchical and Factorial models #3 | Factorial model", "Additive Genetic Effect", "What is an additive genetic effect", "Specifying a pedigree", "Fitting an animal model", "Animal model: results", "Extracting Predicted Breeding Values", "Handling pedigrees", "Spatial autocorrelation", "What is spatial autocorrelation", "Diagnosing spatial autocorrelation | residuals spatial plot", "Diagnosing spatial autocorrelation | variograms of residuals", "Interpreting the variograms", "Accounting for spatial autocorrelation", "The blocks model", "Animal-spatial model: results", "Variogram of residuals", "B-Splines model", "Autoregressive model", "Change in model residuals", "Comparison of spatial components", "Prediction of the spatial effect in unobserved locations", "Spatial parameters | Number of knots of a splines model", "Spatial parameters | Autoregressive parameters of a AR model", "Exercise | Tuning spatial parameters", "Spatial #1 | B-splines model with increased nok", "Spatial #2 | Visualize log-likelihoods", "Spatial #3 | Refine grid", "Competition", "Theoretical model", "Permanent Environmental Effect (pec)", "Simulation of data", "Fitting a competition model", "True vs. estimated parameters", "Exercise | Competition models", "Competition #1 | True vs. predicted components", "Competition #2 | Map of residuals and their variogram", "Generic component", "The Generic model", "Implementation of the generic component", "Example of result", "Prediction", "Predicting values for unobserved trees", "Leave-one-out cross-validation", "Exercise | Cross validation", "Cross-validation #1 | Include prediction with full data", "Cross-validation #2 | Perform cross-validation on 1/10th of the observations", "Cross-validation #3 | MSE of Prediction", "Multiple traits", "Initial (co)variance specification", "Some more features", "Metagene interface", "Simulation framework", "Remote computation", "Package options" ], "created": "2014-09-04 15:43:34", "modified": "2017-04-15 15:49:30", "commits": 16 }, { "source": "Heritability.Rmd", "filename": "Heritability.pdf", "title": "Computing heritability", "author": "Facundo Muñoz", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Case 1: Explicit genetic component with method = 'ai'", "Case 2: Using custom heritability formulae", "2.1 Specifying 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