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  "Description": "Random forests are a statistical learning method widely\nused in many areas of scientific research essentially for its\nability to learn complex relationships between input and output\nvariables and also its capacity to handle high-dimensional\ndata. However, current random forests approaches are not\nflexible enough to handle longitudinal data.  In this package,\nwe propose a general approach of random forests for\nhigh-dimensional longitudinal data. It includes a flexible\nstochastic model which allows the covariance structure to vary\nover time. Furthermore, we introduce a new method which takes\nintra-individual covariance into consideration to build random\nforests. The method is fully detailled in Capitaine et.al.\n(2020) <doi:10.1177/0962280220946080> Random forests for\nhigh-dimensional longitudinal data.",
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  "Author": "Louis Capitaine [aut, cre]\n(<https://orcid.org/0000-0001-6800-2342>)",
  "Maintainer": "Louis Capitaine <Louis.capitaine@u-bordeaux.fr>",
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