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https://github.com/ms609/oreillyetal2016

Data generated from O'Reilly et al. (2016). See Readme below for documentation.
https://github.com/ms609/oreillyetal2016

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Data generated from O'Reilly et al. (2016). See Readme below for documentation.

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README

          

# OReillyEtAl2016

This R package provides data and analyses that accompany the paper

- Martin R. Smith (2019), Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets, _Biological Letters_, 15:20180632.
doi:[10.1098/rsbl.2018.0632](https://dx.doi.org/10.1098/rsbl.2018.0632)

It incorporates data from:

- O’Reilly, J. E., M. N. Puttick, L. Parry, A. R. Tanner, J. E. Tarver, J. Fleming, D. Pisani, and P. C. J. Donoghue. 2016. Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data. Biology Letters, 12:20160081. doi:[10.1098/rsbl.2016.0081](https://dx.doi.org/10.1098/rsbl.2016.0081).

- O’Reilly, J. E., M. N. Puttick, L. Parry, A. R. Tanner, J. E. Tarver, J. Fleming, D. Pisani, and P. C. J. Donoghue. 2016. Data from: Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data. Dryad Digital Repository, doi:[10.5061/dryad.10qf3](https://dx.doi.org/10.5061/dryad.10qf3).

Equivalent data and analyses from data simulated by Congreve & Lamsdell (2016)
are available at [ms609.github.io/CongreveLamsdell2016](https://ms609.github.io/CongreveLamsdell2016).

The documentation has not yet been thoroughly checked for typographical errors;
if you find any issues, paritcularly in the installation instructions, please
[let me know](https://github.com/ms609/OReillyEtAl2016/issues/new) so I can
address them.

## Installation

Install the package in `R` with:
`devtools::install_github('ms609/OReillyEtAl2016')`.

## Documentation

The package contains
[HTML](https://ms609.github.io/OReillyEtAl2016/Conduct-analyses.html) and
[PDF](https://ms609.github.io/OReillyEtAl2016/Conduct-analyses.pdf) files
detailing how analyses were conducted, to facilitate reprodution of results.

The `R` data objects can be analysed using the code and procedures detailed
for the analagous datasets at https://github.com/ms609/CongreveLamsdell2016.