Description: Functions to quickly read LXB parameter data.

Authors@R: c(person("Björn", "Winckler", role = c("aut", "cre"),
person("James K.", "Lawless", role = c("ctb", "cph")))

Title: Accurate Estimation of Clonal Coincidences and Abundances from
Authors@R: c(person("Yi Liu", "Author and Developer", role = c("aut", "cre"), email = "liuyipei@stanford.edu"),
person("Richard Olshen", "Author", role = "aut", email = "olshen@stanford.edu"),
person("Andrew Fire", "Author", role = "ctb", email = "afire@stanford.edu"),
person("Scott Boyd", "Author", role = "ctb", email = "sboyd1@stanford.edu"))
Author: Yi Liu, Richard Olshen, Andrew Fire, Scott Boyd
Description: We provide a clonality score estimator that takes full advantage of the multi-biological-replicate structure of modern sequencing experiments; it specifically takes into account the reality that, typically, the clonal coverage is well below 0.1%.

Authors@R: c(person("Yi", "Liu", role = c("aut", "cre"), email = "liuyipei@stanford.edu"),
person("Richard A.", "Olshen", , role = "aut", email = "olshen@stanford.edu"),
person("Andrew Z.", "Fire",  role = "aut", email = "afire@stanford.edu"),
person("Scott D.", "Boyd", role = "aut", email = "sboyd1@stanford.edu"))

Authors@R: c(person(given = "Yi", family = "Liu", role = c("aut", "cre"), email = "liuyipei@gmail.com"),
person(given = "Richard A.", family = "Olshen", role = "aut", email = "olshen@stanford.edu"),
person(given = "Andrew Z.", family = "Fire", role = "ctb", email = "afire@stanford.edu"),
person(given = "Scott D.", family = "Boyd", role = "ctb", email = "sboyd1@stanford.edu"))

Title: Movement to Behaviour Inference using Random Forest
Description: Prediction of behaviour from movement
characteristics using observation and random forest for the analyses of movement
From movement information (speed, bearing...) the model predicts the
observed behaviour (movement, foraging...) using random forest. The
model can then extrapolate behavioural information to movement data
without direct observation of behaviours.
The specificity of this method relies on the derivation of multiple predictor variables from the
movement data over a range of temporal windows. This procedure allows to capture
as much information as possible on the changes and variations of movement and
ensures the use of the random forest algorithm to its best capacity. The method
is very generic, applicable to any set of data providing movement data together with
Imports: geosphere, caTools, ggplot2, randomForest, e1071, caret,
Suggests: adehabitatLT, moveHMM, knitr, DiagrammeR, rmarkdown