network, coda, MCMCpack, Matrix, mvtnorm, matrixcalc,
statnet.common, Rglpk

R Emulation of Berkeley Earth Surface Temperature
A selection of functioned designed to emulate the Berkeley
Earth Surface Temperature Project. Version 1 includes only
those function to import data.

R (>= 2.11.0), bigmemory, biganalytics, RghcnV3

A selection of functioned designed to emulate the Berkeley
Earth Surface Temperature Project. Version 1.2 includes only
those function to import data.

Data Input for Berkeley Earth Surface Temperature
A selection of functioned designed to import the Berkeley
Earth Surface Temperature Project. Version 1.5 includes only
those function to import data.Additional packages for analysis
are forthcoming.

This package provides easy access to http://daten.berlin.de
It allows you to search through the data catalogue and to download
the data directly from within R.

Bessel -- Bessel Functions Computations and Approximations
Bessel Function Computations for complex and real numbers;
notably interfacing TOMS 644; approximations for large
arguments, experiments, etc.

Bessel Function Computations for complex and real numbers;
notably interfacing TOMS 644; approximations for large arguments,
experiments, etc.

Computations and Approximations for Bessel Functions
Computations for Bessel function for complex, real and partly
'mpfr' (arbitrary precision) numbers; notably interfacing TOMS 644;
approximations for large arguments, experiments, etc.
only 'gsl' may be used in code; the others are for
vignettes only

Bayesian Estimation of Intervention Effects
Jack Kuipers [aut,cre] and Giusi Moffa [aut]
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (up to around 20 variables) and Monte Carlo schemes (for larger networks) are implemented.

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.