Set of tools to verify forecasts through the computation of typical
prediction scores against one or more observational datasets or reanalyses (a
reanalysis being a physical extrapolation of observations that relies on the
equations from a model, not a pure observational dataset). Intended for seasonal
to decadal climate forecasts although can be useful to verify other kinds of
forecasts. The package can be helpful in climate sciences for other purposes
than forecasting.

c(person("Juan C.",
"Laria",,
role = c("aut", "cre"),
email = "juank.laria@gmail.com",
comment = c(ORCID = "0000-0001-7734-9647")),
person("Line H.",
"Clemmensen",,
role = c("aut"),
email = "lkhc@dtu.dk"))
Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
knitr, rmarkdown, glmnet, Metrics, testthat
Juan C. Laria [aut, cre] (<https://orcid.org/0000-0001-7734-9647>),
Line H. Clemmensen [aut]

Resources are files in tidy or R data format stored in a S3 compatible system, such as
Amazon Web Services S3 or Minio object stores. Resources can also be Parquet files, accessed through
an Apache Spark service.
R6, httr, resourcer (>= 1.2), aws.s3, sparklyr

A S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio).
The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake)
Parquet file to be read by Apache Spark.