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  "Title": "Model-Based Clustering for Functional Data with Covariates",
  "Date": "2025-03-31",
  "Description": "Routines for model-based functional cluster analysis for\nfunctional data with optional covariates. The idea is to\ncluster functional subjects (often called functional objects)\ninto homogenous groups by using spline smoothers (for\nfunctional data) together with scalar covariates. The spline\ncoefficients and the covariates are modelled as a multivariate\nGaussian mixture model, where the number of mixtures\ncorresponds to the number of clusters. The parameters of the\nmodel are estimated by maximizing the observed mixture\nlikelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna,\n2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is\nused to analyze annual lake sediment from lake Kassjön\n(Northern Sweden) which cover more than 6400 years and can be\nseen as historical records of weather and climate.",
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