Year: 2026
Type: dataset
Abstract: Provides tools for estimation and prediction using Maximum Agreement Linear Predictors (MALPs). MALPs provide an alternative to least squares linear predictors when agreement between predicted and observed values, as measured by Lin's Concordance Correlation Coefficient (CCC), is of primary interest. Applications include missing value imputation and calibration studies. The package includes functions for model estimation, prediction, statistical inference, cross-validation, and model diagnostics. The implemented methodology is described in Kim et al. (2026) <doi:10.1214/26-EJS2550>.
Authors: Pierre Chausse, Taeho Kim, Edsel A. Pena
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7172285892