DataVisualizations, DatabionicSwarm, rgl, grid, mgcv, png,
ProjectionBasedClustering, reshape2, fields, ABCanalysis,
plotly, deldir, shiny, methods, knitr (>= 1.12), rmarkdown (>=
0.9)

DataVisualizations, rgl, grid, mgcv, png, reshape2, fields,
ABCanalysis, plotly, deldir, methods, knitr (>= 1.12),
rmarkdown (>= 0.9)

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.

C++11, pandoc (>=1.12.3, needed for vignettes)

c(person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph"), comment = c(ORCID = "0000-0001-9542-5543")),person("Felix", "Pape",role=c("ctb","ctr")),person("Tim", "Schreier",role=c("ctb","ctr")),person("Luis", "Winckelman",role=c("ctb","ctr")),person("Quirin", "Stier",role=c("ctb","ctr")),person("Alfred", "Ultsch",role="ths"))
Rcpp (>= 1.0.8), RcppParallel (>= 5.1.4), ggplot2
C++11, GNU make, pandoc (>=1.12.3, needed for
vignettes)
Michael Thrun [aut, cre, cph] (<https://orcid.org/0000-0001-9542-5543>),
Felix Pape [ctb, ctr],
Tim Schreier [ctb, ctr],
Luis Winckelman [ctb, ctr],
Quirin Stier [ctb, ctr],
Alfred Ultsch [ths]

Fully Parameterized Generalized Wendland Covariance Function
c(
person(c("Thomas", "C."), "Fischer", role = c("aut", "cre"), email = "thomascasparfischer@gmail.com"),
person("Reinhard", "Furrer", role = c("aut", "ths"), email = "reinhard.furrer@math.uzh.ch"),
person("Josef", "Stocker", role = c("aut"), email = "josef.stocker@posteo.ch"))
methods, stats, utils, Matrix, Rcpp, spam, spam64, parallel,
optimParallel, fields
knitr, R.rsp, testthat (>= 3.0.0), mvtnorm, ggplot2,
gridExtra, dplyr, microbenchmark
A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation.
Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) <url:<https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F17-AOS1652 >>.
Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) <arXiv:2008.02904>.
Reinhard Furrer and Roman Flury and Florian Gerber (2022) <url:<https://CRAN.R-project.org/package=spam >>.
Thomas C. Fischer [aut, cre],
Reinhard Furrer [aut, ths],
Josef Stocker [aut]