The BGLR R package provides a unified Bayesian framework for genome-wide regression and prediction, supporting various parametric and nonparametric models. It is particularly useful for high-dimensional data where the number of predictors (p) exceeds the sample size (n). The package includes Bayesian ridge regression (BRR), BayesA, and BayesB models, which incorporate shrinkage and variable selection. It also supports reproducing kernel Hilbert spaces (RKHS) regressions, which are useful for nonparametric genomic analysis. The package is implemented using efficient C and Fortran routines, enabling fast computation. It can handle continuous and categorical responses, including censored data and binary/ordinal outcomes. The BGLR package allows users to combine different modeling approaches and provides tools for model comparison and prediction accuracy assessment. It is available for use in genomic and non-genomic applications, and includes example datasets and functions for data analysis. The package is designed to be flexible and efficient, with support for large-scale data and complex genetic models.The BGLR R package provides a unified Bayesian framework for genome-wide regression and prediction, supporting various parametric and nonparametric models. It is particularly useful for high-dimensional data where the number of predictors (p) exceeds the sample size (n). The package includes Bayesian ridge regression (BRR), BayesA, and BayesB models, which incorporate shrinkage and variable selection. It also supports reproducing kernel Hilbert spaces (RKHS) regressions, which are useful for nonparametric genomic analysis. The package is implemented using efficient C and Fortran routines, enabling fast computation. It can handle continuous and categorical responses, including censored data and binary/ordinal outcomes. The BGLR package allows users to combine different modeling approaches and provides tools for model comparison and prediction accuracy assessment. It is available for use in genomic and non-genomic applications, and includes example datasets and functions for data analysis. The package is designed to be flexible and efficient, with support for large-scale data and complex genetic models.