Title: Data Representation: Bayesian Approach That's Sparse
Authors@R: c(person("Gabrielle", "Weinrott", email = "gabrielle.weinrott@supagro.inra.fr", role = c("aut", "cre")), person("Brigitte", "Charnomordic", email = "bch@supagro.inra.fr", role = "aut"), person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = "aut"), person("Nadine", "Hilgert", email = "nadine.hilgert@supagro.inra.fr", role = "aut"), person("Susan", "Holmes", email = "susan@stat.stanford.edu", role = "aut"))
Description: Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Imports: ade4, coda, MASS, Matrix, rstan, sde
Suggests: fda, ggplot2, knitr, parallel, rmarkdown, testthat

Authors@R: c(person("Anne", "Bisson", email = "anne.bisson@inra.fr", role = "cre"), person("Gabrielle", "Weinrott", email = "gabrielle.weinrott@supagro.inra.fr", role = "aut"), person("Brigitte", "Charnomordic", email = "bch@supagro.inra.fr", role = "aut"), person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = "aut"), person("Nadine", "Hilgert", email = "nadine.hilgert@supagro.inra.fr", role = "aut"), person("Susan", "Holmes", email = "susan@stat.stanford.edu", role = "aut"))

person("Gabrielle", "Weinrott", email = "gabrielle.weinrott@supagro.inra.fr", role = "aut"),
person("Brigitte", "Charnomordic", email = "bch@supagro.inra.fr", role = "ctr"),
person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = c("cre", "aut")),
person("Nadine", "Hilgert", email = "nadine.hilgert@inrae.fr", role = "ctr"),
person("Susan", "Holmes", email = "susan@stat.stanford.edu", role = "ctr"),
person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = "ctr"))
Description: Feed longitudinal data into a Bayesian Latent Factor Model to obtain
a low-rank representation. Parameters are estimated using a Hamiltonian
Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and
S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis",