Susanne Gammelgaard Bøttcher <alma@math.aau.dk>, Claus Dethlefsen <cld@rn.dk>.
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

Susanne Gammelgaard Bottcher <alma@math.aau.dk>, Claus Dethlefsen <cld@rn.dk>.

Susanne Gammelgaard Bottcher <lma@math.aau.dk>, Claus Dethlefsen <cld@rn.dk>.
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

Susanne Gammelgaard Bottcher <alma@math.aau.dk>, Claus
Dethlefsen <cld@rn.dk>.
Bayesian networks with continuous and/or discrete
variables can be learned and compared from data.

Susanne Gammelgaard Bottcher, Claus Dethlefsen.

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>.

Deconvolution density estimation with adaptive methods for a
variable prone to measurement error
Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution
from Claire Lacour.
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
penalized contrasts.

A Post-Clustering Denoiser for COI-5P Barcode Data
The 'debar' sequence processing pipeline is designed for denoising high throughput
sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package
is designed to detect and correct insertion and deletion errors within sequencer outputs.
This is accomplished through comparison of input sequences against a profile hidden Markov
model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998,
ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted
for through the introduction of a placeholder character. Since the PHMM is a probabilistic
representation of the COI barcode, corrections are not always perfect. For this reason
'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder
characters (default is 7 base pairs in either direction, this feature can be disabled).
Testing has shown that this censorship results in the correct sequence length being restored,
and erroneous base pairs being masked the vast majority of the time (>95%).