data.table, dbscan, dplyr, geosphere (>= 1.5), leaflet (>=
2.0), lubridate (>= 1.7.4), magrittr, mapedit, methods, nnet,
rgdal (>= 1.5), rgeos (>= 0.4), rlang, sf (>= 0.7), stringr (>=
1.4), shotGroups, taxize, tidyr, xts, zoo
covr, knitr, leaflet.extras, leafpm, maps, rmarkdown, shiny
(>= 1.0), testthat, utils, vegan

Programmatic Access to Bank for International Settlements Data
person("Eric", "Persson", email = "expersso5@gmail.com",
role = c("aut", "cre"))
Provides an interface to data provided by the Bank for International
Settlements <https://www.bis.org>, allowing for programmatic retrieval of a
large quantity of (central) banking data.

c(
person("Eric", "Persson", role = c("aut")),
person("Stefan", "Angrick", email = "contact@stefanangrick.net",
role = c("cre"))
)
Provides an interface to data provided by the Bank for
International Settlements <https://www.bis.org>, allowing for programmatic
retrieval of a large quantity of (central) banking data.
dplyr, readr, tidyr, tidyselect, rvest, xml2
Eric Persson [aut],
Stefan Angrick [cre]

Download Data from the Bank for International Settlements (BIS)
person("Enrico", "Schumann",
role  = c("aut", "cre"),
email = "es@enricoschumann.net",
comment = c(ORCID = "0000-0001-7601-6576"))
Functions for downloading data from the Bank for
International Settlements (BIS; <https://www.bis.org/>) in
Basel.  Supported are only full datasets in (typically) CSV
format.  The package is lightweight and without dependencies;
suggested packages are used only if data is to be transformed
into particular data structures, for instance into 'zoo'
objects. Downloaded data can optionally be cached, to avoid
repeated downloads of the same files.
Enrico Schumann [aut, cre] (<https://orcid.org/0000-0001-7601-6576>)

http://enricoschumann.net/R/packages/BISdata/ ,
https://git.sr.ht/~enricoschumann/BISdata ,
https://github.com/enricoschumann/BISdata

Bayesian kernel projection classifier is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.