c(person("Pete", "Mohanty", role = c("aut", "cre"),
email = "pete.mohanty@gmail.com"),
person("Robert", "Shaffer", role = "aut",
email = "rbshaffer@utexas.edu"))
Pete Mohanty [aut, cre],
Robert Shaffer [aut]

Functions for Kernel-Regularized Least Squares optimized for speed and memory usage are provided along with visualization tools.
For working papers, sample code, and recent presentations visit <https://sites.google.com/site/petemohanty/software/>. bigKRLS, as well its dependencies, require current versions of R and its compilers (and RStudio if used). For details, see <https://github.com/rdrr1990/code/blob/master/bigKRLS_installation.md>.
bigalgebra, biganalytics, ggplot2, parallel, Rcpp (>= 0.12.4),
shiny

c(person("Pete", "Mohanty", role = c("aut", "cre"), email = "pete.mohanty@gmail.com"), person("Robert", "Shaffer", role = "aut", email = "rbshaffer@utexas.edu"))
Functions for Kernel-Regularized Least Squares optimized for speed and memory usage are provided along with visualization tools. For working papers, sample code, and recent presentations visit <https://sites.google.com/site/petemohanty/software/>. bigKRLS, as well its dependencies, require current versions of R and its compilers (and RStudio if used). For details, see <https://github.com/rdrr1990/bigKRLS/blob/master/INSTALL.md>.
Pete Mohanty [aut, cre], Robert Shaffer [aut]

c(person("Pete", "Mohanty", role = c("aut", "cre"), email = "pete.mohanty@gmail.com", comment = c(ORCID = "0000-0002-4985-5160")),
person("Robert", "Shaffer", role = "aut", email = "shafferr@upenn.edu", comment = c(ORCID = "0000-0002-2081-2407")))

c(person("Pete", "Mohanty", role = c("aut", "cre"), email = "pete.mohanty@gmail.com", comment = c(ORCID = "0000-0001-8531-3345")),
person("Robert", "Shaffer", role = "aut", email = "shafferr@upenn.edu", comment = c(ORCID = "0000-0002-2081-2407")))

c(
person(given="Joan", family="Garriga", email="jgarriga@ceab.csic.es", role=c("aut", "cre")),
person(given="Frederic", family="Bartumeus", email="fbartu@ceab.csic.es", role=c("aut"))
)
Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).
Rcpp (>= 0.12.0), bigmemory (>= 4.5.0), parallel (>= 3.5.0),
RColorBrewer, colorspace,
snow (>= 0.4-2), Rmpi (>= 0.6-7), knitr, rmarkdown
Joan Garriga [aut, cre],
Frederic Bartumeus [aut]