Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.

A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
    This package is based on Cameron A., Conraths F., Frohlich A., Schauer B.,
    Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of 
    functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision 
    making tools for implementing risk-based surveillance, Grant Number 
    no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). 
    Many of the 'RSurveillance' functions are incorporated into the 'epitools'
    website: Sergeant, ESG, 2019. Epitools epidemiological calculators. 
    Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.

'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.

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.