Data sets and code blocks for the book 'Statistical Analysis of 
  Network Data with R, 2nd Edition'.

A flexible framework for definition and application of time/depth-
    based rules for sets of parameters for single grains that can be used to 
    create artificial sediment profiles. Such profiles can be used for virtual 
    sample preparation and synthetic, for instance, luminescence measurements.

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic 
             robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
	     covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
	     covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
	     clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
	     outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
	     applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
	     to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>,
	     Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.

Downloads and tidies the San Francisco Public Utilities Commission Beach Water Quality Monitoring Program data. Data sets can be downloaded per beach, or the raw data can be downloaded. See <https://sfwater.org/cfapps/lims/beachmain1.cfm>.

Created for population health analytics and monitoring.
    The functions in this package work best when working with patient level Master Patient Index-like datasets .
    Built to be used by  NHS bodies and other health service providers.

Routines for solving large systems of linear equations and
  eigenproblems in R. Direct and iterative solvers from the Eigen C++ library
  are made available. Solvers include Cholesky, LU, QR, and Krylov subspace
  methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are
  supported.