In this article of Huaihou Chen and Yuanjia Wang, they proposed penalized spline (P-spline)-based methods for functional mixed effects models with varying coefficients. They decomposed longitudinal outcomes as a sum of several terms: a population mean function, covariateswith time-varying coefficients, functional subject-specific random effects, and residual measurement error processes. Proposed methods offer flexible estimation of both the population- and subject-level curves. In addition, decomposing variability of the outcomes as a between- and within-subject source is useful in identifying the dominant variance component therefore optimally model a covariance function.The benefit of the between- and within-subject covariance decomposition is illustrated through an analysis of Berkeley growth data, where they identified clearly distinct patterns of the between- and within-subject covariance functions of children’s heights.