'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.

Provides an interface to the 'SurvStat' web service from the
  Robert Koch Institute (<https://tools.rki.de/SurvStat/SurvStatWebService.svc>) 
  allowing downloads of disease time series stratified
  by pathogen type and subtype, age, and geography from notifiable disease
  reports in Germany.

Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided.

Computing singular value decomposition with robustness is a challenging task. 
    This package provides an implementation of computing robust SVD using density power 
    divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation
    based on a tuning parameter. It also provides utility functions to simulate various
    scenarios to compare performances of different algorithms.