MECfda: An R Package for Bias Correction Due to Measurement Error in Functional and Scalar Covariates in Scalar-on-Function Regression Models

Abstract

Functional data analysis (FDA) deals with high-resolution data recorded over a continuum, such as time, space or frequency. Device-based assessments of physical activity or sleep are objective yet still prone to measurement error. We present MECfda, an R package that (i) fits scalar-on-function, generalized scalar-on-function, and functional quantile regression models, and (ii) provides bias-corrected estimation when functional covariates are measured with error. By unifying these tools under a consistent syntax, MECfda enables robust inference for FDA applications that involve noisy functional data.