Grace Chiu <bentcable@gmail.com>,
Virginia Institute of Marine Science, William & Mary
PO Box 1346, Gloucester Point, VA 23062, USA
Included are two main interfaces, bentcable.ar() and
bentcable.dev.plot(), for fitting and diagnosing bent-cable
regressions for autoregressive time-series data (Chiu and
Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time
series or otherwise - Chiu, Lockhart and Routledge 2006,
<doi:10.1198/016214505000001177>). Some components in the package
can also be used as stand-alone functions. The bent cable
(linear-quadratic-linear) generalizes the broken stick
(linear-linear), which is also handled by this package. Version
0.2 corrected a glitch in the computation of confidence intervals
for the CTP. References that were updated from Versions 0.2.1 and
0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved
robustness of the error-message producing mechanism. Version 0.3.1
improves the NAMESPACE file of the package. It is the author's
intention to distribute any future updates via GitHub.

Preprocessing tools and biodiversity measures
(species abundance, species richness, population heterogeneity and
sensitivity) for analysing marine benthic data. See Van Loon et al.
(2015) <doi:10.1016/j.seares.2015.05.002> for an application of
these tools.
person(given = "Dennis", family = "Walvoort",
email = "dennis.Walvoort@wur.nl", role = c("aut", "cre", "cph"))
dplyr (>= 0.7.0), lazyeval, readr, utils
testthat (>= 1.0.0), rmarkdown, knitr, ggplot2, tidyr

testthat (>= 2.1.0), rmarkdown, knitr, ggplot2, tidyr

In this package there are functions for adjusting the data
to remove batch effects

The functions in this package remove batch effects from
microarrary normalized data. The expression levels of the genes
are represented in a matrix where rows correspond to
independent samples and columns to genes (variables). The
batches are represented by categorical variables (objects of
class factor). When further covariates of interest are
available they can be used to remove efficiently the batch
effects and adjust the data.