or more metabolic rate traits. The R package is ideally integrated with the pump
controller 'PumpResp' and the DO meter 'SensResp' (open-source hardware by FishResp).
Raw respirometry data can be also imported from 'AquaResp' (free software), 'AutoResp'
('LoligoSystems'), 'OxyView' ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and
'Q-box Aqua' ('QubitSystems'). More information about the R package 'FishResp'is
available in the publication by Morozov et al. (2019) <doi:10.1093/conphys/coz003>.
person("Sergey", "Morozov", role = c("aut", "cre"), email = "sergey.morozov@helsinki.fi"),
person("Scott", "McCairns", role = c("aut", "ths")),
person("Jenni", "Prokkola", role = "dtc"),
person("Alexander", "Kotrschal", role = "dtc"),

Title: Model-Based Clustering in the Fisher Discriminative Subspace
Author: J.Loisel T.Souvannarath M.Tchebanenko C.Bouveyron C.Brunet
Maintainer: Who to complain to <thanh.souvannarath@gmail.com>
Description: Model-Based Clustering in the Fisher Discriminative
Subspace provides a low-dimensional discriminative
representation of the clustered data. To find a parsimonious
and discriminative fit for the data this method uses
discriminative latent model (DLM). The Fisher EM algorithm
estimates the parameters of DLM models in order to cluster and

Author: Charles Bouveyron and Camille Brunet
Description: The FisherEM package provides an efficient algorithm for
the unsupervised classification of high-dimensional data. This
FisherEM algorithm models and clusters the data in a
discriminative and low-dimensional latent subspace. It also
provides a low-dimensional representation of the clustered

data. A sparse version of Fisher-EM algorithm is also provided.

Title: The FisherEM Algorithm to Simultaneously Cluster and Visualize
Description: The FisherEM algorithm, proposed by Bouveyron & Brunet (201) <doi:10.1007/s11222-011-9249-9>,
is an efficient method for the clustering of high-dimensional data. FisherEM models and
clusters the data in a discriminative and low-dimensional latent subspace. It also provides
a low-dimensional representation of the clustered data. A sparse version of Fisher-EM