Implements a system of linear equations to recover
    unreported diagnostic test accuracy cell counts from commonly
    reported measures such as sensitivity, specificity, predictive
    values, prevalence, and sample size. The package is intended for
    applied researchers who require complete 2x2 table counts for
    downstream analyses.

Fast functions for effective sample size, weighted multivariate 
    mean, variance, and quantile computation, and weight diagnostic plot for 
    generic importance sampling type or other probability weighted samples.

Diagnostics for linear L1 regression (also known as LAD - Least Absolute Deviations), including: estimation, confidence intervals, tests of hypotheses, measures of leverage, methods of diagnostics for L1 regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005)  <doi:10.1080/0094965042000223680>, Elian et al. (2000) <doi:10.1080/03610920008832518> and Dodge (1997) <doi:10.1006/jmva.1997.1666>. This package builds on the 'quantreg' package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) <doi:10.2307/1270952>.

Provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.

Several tools for handling block-matrix diagonals and similar
    constructs are implemented. Block-diagonal matrices can be extracted or removed
    using two small functions implemented here. In addition, non-square matrices
    are supported. Block diagonal matrices occur when two dimensions of a data set
    are combined along one edge of a matrix. For example, trade-flow data in the
    'decompr' and 'gvc' packages have each country-industry combination occur along
    both edges of the matrix.

Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, etc.
     Support for the book "A practical guide to ecological modelling -
     using R as a simulation platform"
     by Karline Soetaert and Peter M.J. Herman (2009), Springer.
     and the book "Solving Differential Equations in R"
     by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb).