c(person("Olga","Permiakova", role = c("aut", "cre"), email = "olga.permiakova@gmail.com"),
person("Thomas","Burger",role = "aut",email = "thomas.burger@cea.fr"))
Toolbox for learning a dictionary from large-scale data collection
using the Sketched Stochastic Dictionary Learning method (see Permiakova O, Burger T.
"Sketched Stochastic Dictionary Learning for large-scale data and application to large-scale
mass spectrometry data", 2021). It includes the routines for the dictionary initialization.
Rcpp, bigstatsr (>= 1.2.3), chickn (>= 1.2.3), RcppParallel,
glmnet, parallel, foreach, doParallel, doRNG, reshape2, stats,
utils, pracma, Rdpack
Olga Permiakova [aut, cre],
Thomas Burger [aut]

Sylvain Schmitt, Robin Pouteau, Dimitri Justeau, Philippe Birnbaum
Allows to map species richness and endemism based on stacked
species distribution models (SSDM). Individuals SDMs can be created using a
single or multiple algorithms (ensemble SDMs). For each species, an SDM can
yield a habitat suitability map, a binary map, a between-algorithm variance
map, and can assess variable importance, algorithm accuracy, and between-
algorithm correlation. Methods to stack individual SDMs include summing
individual probabilities and thresholding then summing. Thresholding can be
based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
distribution. The SSDM package also provides a user-friendly interface.
'Algorithm.SDM.R' 'Ensemble.SDM.R' 'Env.R' 'Occurrences.R'
'SSDM.R' 'Stacked.SDM.R' 'checkargs.R' 'ensemble.R'
'modelling.R' 'ensemble_modelling.R' 'gui.R' 'load_model.R'
'load_occ.R' 'load_var.R' 'plot.model.R' 'save.model.R'
'stack_modelling.R' 'stacking.R' 'update.stack.R' 'zzz.R'

'SDM.R' 'Algorithm.SDM.R' 'Ensemble.SDM.R' 'Env.R'
'Occurrences.R' 'PA.select.R' 'SSDM.R' 'Stacked.SDM.R'
'checkargs.R' 'data.values.R' 'ensemble.R' 'modelling.R'
'ensemble_modelling.R' 'evaluate.R' 'evaluate.axes.R'
'get_PA.R' 'get_model.R' 'gui.R' 'load_model.R' 'load_occ.R'
'load_var.R' 'mapDiversity.R' 'plot.model.R' 'project.R'
'save.model.R' 'stack_modelling.R' 'stacking.R'
'update.stack.R' 'zzz.R'