This paper presents an R package, fdaMocca, that provides routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm. The usefulness of fdaMocca and its clustering methods is illustrated on a functional data set with covariates from 6400 annual seasonal patterns of varved lake sediment from Lake Kassjön (Northern Sweden). Each varve contains information about the weather the year the varve was formed and thus may be used to reconstruct past climate.