PCMBaseCpp

This is a fast C++ backend for the PCMBase R-package.

Installation

The package needs a C++ 11 compiler and Rcpp to be installed in you R-environment. Once this is done, you can install the most recent version of the package from github:

devtools::install_github("venelin/PCMBaseCpp")

If you experience problems installing the package from github, you may try installing a possibly older version from CRAN:

install.packages("PCMBaseCpp")

Once the package is installed, use the function BenchmarkRvsCpp to evaluate the gain in speed of the likelihood calculation on your machine, relative to the R implementation:

library(PCMBaseCpp)
library(data.table)
options(digits = 4)

# Depending on your use case, you can change the number of traits, as well as the 
# other arguments:
benchRes <- BenchmarkRvsCpp(ks = 2, includeParallelMode = FALSE, verbose = TRUE)
# Example output:
# Performing benchmark for k:  2 ; optionSet:  serial / 1D-multiv. ...
#     k  modelType     N  R mode     logLik  logLikCpp  timeR timeCpp
#  1: 2 MGPM (A-F)    10  2   11 -7.416e+02 -7.416e+02  0.010  0.0007
#  2: 2 MGPM (A-F)   100  4   11 -4.294e+03 -4.294e+03  0.107  0.0016
#  3: 2 MGPM (A-F)  1000 11   11 -1.700e+05 -1.700e+05  1.221  0.0095
#  4: 2 MGPM (A-F) 10000 11   11 -1.210e+06 -1.210e+06 12.443  0.0795
#  5: 2     BM (B)    10  2   11 -4.451e+03 -4.451e+03  0.010  0.0003
#  6: 2     BM (B)   100  4   11 -8.427e+03 -8.427e+03  0.082  0.0008
#  7: 2     BM (B)  1000 11   11 -1.830e+04 -1.830e+04  0.847  0.0064
#  8: 2     BM (B) 10000 11   11 -6.574e+05 -6.574e+05  8.414  0.0663
#  9: 2     OU (E)    10  2   11 -1.126e+04 -1.126e+04  0.016  0.0006
# 10: 2     OU (E)   100  4   11 -8.486e+05 -8.486e+05  0.147  0.0015
# 11: 2     OU (E)  1000 11   11 -1.234e+06 -1.234e+06  1.505  0.0096
# 12: 2     OU (E) 10000 11   11 -1.058e+07 -1.058e+07 15.062  0.0854

For further examples, read the Getting started guide and the reference available on the package homepage.

Citing PCMBase

To give credit to the PCMBase package in a publication, please cite the following articles:

Mitov, V., & Stadler, T. (2018). Parallel likelihood calculation for phylogenetic comparative models: The SPLITT C++ library. Methods in Ecology and Evolution, 2041–210X.13136. http://doi.org/10.1111/2041-210X.13136

Mitov, V., Bartoszek, K., Asimomitis, G., & Stadler, T. (2019). Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts. Theor. Popul. Biol. https://doi.org/10.1016/j.tpb.2019.11.005

Used 3rd party libraries

The PCMBaseCpp R-package uses the following R-packages and C++ libraries:

• For tree processing in C++: The SPLITT library (Mitov and Stadler 2018);
• For data processing in R: data.table v1.12.8 (Dowle and Srinivasan 2019);
• For algebraic manipulation: The Armadillo C++ template library (Sanderson and Curtin 2016) and its port to R RcppArmadillo v0.9.700.2.0 (Eddelbuettel et al. 2019);
• For unit-testing: testthat v2.1.1 (Wickham 2019), covr v3.2.1 (Hester 2018);
• For documentation and web-site generation: roxygen2 v6.1.1 (Wickham, Danenberg, and Eugster 2018), pkgdown v1.3.0 (Wickham and Hesselberth 2018);

Licence and copyright

Copyright 2016-2020 Venelin Mitov

Source code to PCMBaseCpp is made available under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. PCMBaseCpp is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

References