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  "Title": "Implement Fleming-Viot-Dependent Dirichlet Processes",
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  "Authors@R": "person(\"Stefano\", \"Damato\", email = \"stefano.damato@idsia.ch\", role = c(\"aut\", \"cre\"))",
  "Description": "A Bayesian Nonparametric model for the study of\ntime-evolving frequencies, which has become renowned in the\nstudy of population genetics. The model consists of a Hidden\nMarkov Model (HMM) in which the latent signal is a\ndistribution-valued stochastic process that takes the form of a\nfinite mixture of Dirichlet Processes, indexed by vectors that\ncount how many times each value is observed in the population.\nThe package implements methodologies presented in Ascolani,\nLijoi and Ruggiero (2021) <doi:10.1214/20-BA1206> and Ascolani,\nLijoi and Ruggiero (2023) <doi:10.3150/22-BEJ1504> that make it\npossible to study the process at the time of data collection or\nto predict its evolution in future or in the past.",
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      "page": "approx.propagate",
      "title": "Approximate the propagation of a Fleming-Viot latent signal",
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      "title": "Approximate the smoothing distribution of a Fleming-Viot latent signal",
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      "title": "Compare the performance of a Monte-Carlo estimate with respect to the exact result.",
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    },
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      "title": "Initialize Fleming-Viot dependent Dirichlet Processes by setting hyperparameters",
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