Imports: graphics, grDevices, spdep, maptools, rgdal, rlang, methods,

person("Juerg", "Guggenbuehl", role = c("ctb")),
person("Jeffery S.", "Evans", role = c("ctb")))

Depends: base, stats, R (>= 4.0.0), sp, DescTools

Authors@R: person("Patrick", "Sturm", email = "sturm@tofwerk.com", role = c("aut", "cre"))
Description: Provides the solution from York (1968) <doi:10.1016/S0012-821X(68)80059-7>
for fitting a straight line to bivariate data with errors in both coordinates.
It gives unbiased estimates of the intercept, slope and standard errors of the
best-fit straight line to independent points with (possibly correlated)
normally distributed errors in both x and y. Other commonly used
errors-in-variables methods, such as orthogonal distance regression, geometric
mean regression or Deming regression are special cases of York’s solution.

Description: How to fit a straight line through a set of points with errors in
both coordinates? The 'bfsl' package implements the York regression
(York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the
intercept, slope and standard errors for the best-fit straight line to
independent points with (possibly correlated) normally distributed errors in
both x and y. Other commonly used errors-in-variables methods, such as
orthogonal distance regression, geometric mean regression or Deming regression
are special cases of the 'bfsl' solution.

Title: Bayesian Framework for Computational Modelling
Authors@R: person( "Øystein Olav","Skaar", email="bayesianfw@gmail.com", role=c("aut","cre"))
Maintainer: Øystein Olav Skaar <bayesianfw@gmail.com>
Description: Derived from the work of Kruschke (2015, <ISBN:9780124058880>),
the present package aim to provide a framework for conducting Bayesian
analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the
Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <http://mcmc-jags.sourceforge.net/>).
The initial version include several modules for conducting Bayesian
equivalents of chi-squared tests, analysis of variance (ANOVA),
multiple (hierarchical) regression, softmax regression, and for fitting data