The abd package contains data sets and sample code for The
Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009;
Roberts & Company Publishers). Some examples utilize the manipulate package,
which is available within RStudio (http://www.rstudio.com).

boot, car, ggplot2, plyr, HH, ICC, vcd, Hmisc
The abd package contains data sets and sample code for The
Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009;
Roberts & Company Publishers).

person("Kyle", "Bittinger", email = "kylebittinger@gmail.com",
role = c("aut", "cre"))
A collection of measures for measuring ecological diversity.
Ecological diversity comes in two flavors: alpha diversity measures the
diversity within a single site or sample, and beta diversity measures the
diversity across two sites or samples. This package overlaps considerably
with other R packages such as 'vegan', 'gUniFrac', 'betapart', and 'fossil'.
We also include a wide range of functions that are implemented in software
outside the R ecosystem, such as 'scipy', 'Mothur', and 'scikit-bio'.  The
implementations here are designed to be basic and clear to the reader.

c(person(c("Rok","Blagus"),role=c("aut","cre"),email="rok.blagus@mf.uni-lj.si"),person("Sladana", "Babic", role = "ctb",
email = "sladja93babic@gmail.com"))
Rok Blagus [aut, cre],  Sladana Babic [ctb]
Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014) <doi:10.1371/journal.pone.0113677>.

Adaptive Base Error Model in Ultra-Deep Sequencing Data
c(person("Nicola", "Casiraghi", role=c("aut", "cre"), email="casiraghinicola@gmail.com"),
person("Alessandro", "Romanel", role=c("aut")),
person("Francesco", "Orlando", role=c("ctb")),
person("Francesca", "Demichelis", role=c("ctb")))
The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single nucleotide variants (SNVs) in circulating cell free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples.
Nicola Casiraghi [aut, cre],
Alessandro Romanel [aut],
Francesco Orlando [ctb],
Francesca Demichelis [ctb]