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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.