Package: deal
Version: 1.2-39
Date: 2018-10-20
Title: Learning Bayesian Networks with Mixed Variables
Author: Susanne Gammelgaard Bottcher, Claus Dethlefsen.
Maintainer: Claus Dethlefsen <rpackage.deal@gmail.com>
Depends: R (>= 2.0.0)
Description: Bayesian networks with continuous and/or discrete
variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
License: GPL (>= 2)
Collate: addarrows.R autosearch.R conditional.R cycletest.R
drawnetwork.R findex.R generic.R genlatex.R heuristic.R
inspectprob.R jointcont.R jointdisc.R jointprior.R learning.R
makesimprob.R fullsimprob.R maketrylist.R master.R network.R
networkfamily.R node.R numbermixed.R perturb.R postc.R postc0.R
postdist.R readnet.R rnetwork.R savenet.R unique.R
NeedsCompilation: yes
Packaged: 2018-10-20 04:22:24 UTC; clausdethlefsen
Repository: CRAN
Date/Publication: 2018-10-20 07:10:03 UTC

Package: deal
Version: 1.2-42
Date: 2022-11-09
Title: Learning Bayesian Networks with Mixed Variables
Author: Susanne Gammelgaard Bottcher, Claus Dethlefsen.
Maintainer: Claus Dethlefsen <rpackage.deal@gmail.com>
Depends: R (>= 2.0.0)
Description: Bayesian networks with continuous and/or discrete
variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
License: GPL (>= 2)
Collate: addarrows.R autosearch.R conditional.R cycletest.R
drawnetwork.R findex.R generic.R genlatex.R heuristic.R
inspectprob.R jointcont.R jointdisc.R jointprior.R learning.R
makesimprob.R fullsimprob.R maketrylist.R master.R network.R
networkfamily.R node.R numbermixed.R perturb.R postc.R postc0.R
postdist.R readnet.R rnetwork.R savenet.R unique.R
NeedsCompilation: yes
Packaged: 2022-11-09 19:33:37 UTC; clausdethlefsen
Repository: CRAN
Date/Publication: 2022-11-09 21:50:23 UTC

Title: Deconvolution density estimation with adaptive methods for a
Author: Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution
Description: deamer provides deconvolution algorithms for the
non-parametric estimation of the density f of an error-prone
variable x with additive noise e. The model is y = x + e where
the noisy variable y is observed, while x is unobserved.
Estimation may be performed for i) a known density of the error
ii) with an auxiliary sample of pure noise and iii) with an
auxiliary sample of replicate (repeated) measurements.
Estimation is performed using adaptive model selection and