Thepls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R, providing a user-friendly interface modeled after the traditional formula interface. It includes methods for generic functions like predict, update, and coef, as well as specialized functions for scores, loadings, and RMSEP. The package supports various algorithms for PLSR, including the kernel algorithm, orthogonal scores algorithm, and SIMPLS, and offers a flexible cross-validation system. It is designed to be versatile, suitable for both interactive data analysis and as a building block for other functions or packages. The package includes example data sets and detailed documentation, making it accessible to users familiar with R and multivariate regression methods. The article also covers advanced topics such as setting options, using underlying functions directly, and implementation details.Thepls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R, providing a user-friendly interface modeled after the traditional formula interface. It includes methods for generic functions like predict, update, and coef, as well as specialized functions for scores, loadings, and RMSEP. The package supports various algorithms for PLSR, including the kernel algorithm, orthogonal scores algorithm, and SIMPLS, and offers a flexible cross-validation system. It is designed to be versatile, suitable for both interactive data analysis and as a building block for other functions or packages. The package includes example data sets and detailed documentation, making it accessible to users familiar with R and multivariate regression methods. The article also covers advanced topics such as setting options, using underlying functions directly, and implementation details.