c(person("Alexander Luke Spedding", role = c("aut", "cre"),
email = "alexspedding271@gmail.com"),
person("Fabio Cappello", role = "aut"),
person("Giuseppe di Fatta", role = "aut"))
Alexander Luke Spedding [aut, cre],
Fabio Cappello [aut],
Giuseppe di Fatta [aut]

c(
person('Jake', 'Hughey', , 'jakejhughey@gmail.com', c('aut', 'cre')),
person('Robert', 'On', , 'roberton@gmail.com', 'aut'))
'SurveyCTO' is a platform for mobile data collection in offline settings.
The 'rsurveycto' R package uses the 'SurveyCTO' REST API
<https://docs.surveycto.com/05-exporting-and-publishing-data/05-api-access/01.api-access.html>
to read datasets and forms from a 'SurveyCTO' server into R as 'data.table's
and to download file attachments. The package also has limited support to
write datasets to a server.

c(
person('Jake', 'Hughey', , 'jake@agency.fund', c('aut', 'cre')),
person('Robert', 'On', , 'robert@agency.fund', 'aut'))

c(person("N. Benjamin", "Erichson", role = c("aut", "cre"), email = "nbe@st-andrews.ac.uk"))
Randomized singular value decomposition (rsvd) is a very fast probabilistic
algorithm to compute an approximated low-rank singular value decomposition of large
data sets with high accuracy. SVD plays a central role in data analysis and scientific computing.
SVD is also widely used for computing principal component analysis (PCA), a linear dimensionality reduction technique.
Randomized PCA (rpca) is using the approximated singular value decomposition to compute the most significant principal components. In addition several plot functions are provided.

Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.
ggplot2, plyr, scales, grid, testthat, knitr, rmarkdown