Preparing Release 1.0.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: PCMFit
 Type: Package
 Title: Maximum likelihood fit and selection of phylogenetic comparative models
-Version: 0.6.0
+Version: 1.0.0
 Authors@R: c(person("Venelin", "Mitov", email = "vmitov@gmail.com",
                  role = c("aut", "cre", "cph")))
 Maintainer: Venelin Mitov <vmitov@gmail.com>
@@ -26,8 +26,7 @@ Description: The goal of PCMFit is to provide a generic tool for inference and
   perform model selection of the best MGPM for a given tree and data according 
   to an information loss function such as the Akaike information criterion 
   (AIC). The package has been thoroughly tested and applied to real data in the 
-  related research article entitled "Automatic Generation of Evolutionary 
-  Hypotheses using Mixed Gaussian Phylogenetic Models" (currently in review). 
+  related research article Mitov et al. 2019 <doi:10.1073/pnas.1813823116>. 
   Currently, the package is available from <https://github.com/venelin/PCMFit>. 
   The web-page <https://venelin.github.io/PCMFit/> provides access to 
   documentation and related resources.

R/PCMFitMixed.R

@@ -1,14 +1,19 @@
-#' Maximum likelihood based search for an optimal mixed Gaussian phylogenetic
-#' model, given  a tree, trait measurements at its tips and a score function.
+#' Optimal information score search for a mixed Gaussian phylogenetic
+#' model, given a tree, trait measurements at its tips and a score function.
 #'
 #' @inheritParams PCMFit
 #'
-#' @description A mixed Gaussian phylogenetic model (MGPM) represents
-#' Fit regime-assignments to (sub-)trees in a tree with different assigned model
-#' types to each regime.This function performs multiple model fits of mixed regime models
-#' (MixedGaussian) mapping different model-types (e.g. BM and OU) to different
-#' regimes (colors) in a tree and testing different regime assignments to the
-#' branches in the tree.
+#' @description A mixed Gaussian phylogenetic model (MGPM) represents a Gaussian
+#' phylogenetic model with shifts in the underlying parameters and, optionally,
+#' type of Gaussian stochastic process (e.g. shifts from a BM to an OU model of
+#' evolution). Formally, an MGPM consists of the following components:
+#' \itemize{
+#' \item A shift-point configuration: this is a subset of the nodes in the tree
+#' including at least the root node
+#' \item b
+#' }
+#'
+#'
 #' @importFrom foreach foreach when %do% %dopar% %:%
 #' @importFrom data.table data.table rbindlist is.data.table setkey :=
 #' @importFrom PCMBase PCMTree PCMTreeSetLabels PCMTreeSetPartition PCMTreeEvalNestedEDxOnTree PCMTreeNumTips PCMTreeListCladePartitions PCMTreeListAllPartitions PCMTreeToString MixedGaussian PCMOptions PCMTreeTableAncestors PCMTreeSplitAtNode PCMGetVecParamsRegimesAndModels MGPMDefaultModelTypes PCMGenerateModelTypes is.Transformable

README.Rmd

@@ -43,8 +43,7 @@ The goal of PCMFit is to provide a generic tool for inference and
   perform model selection of the best MGPM for a given tree and data according 
   to an information loss function such as the Akaike information criterion 
   (AIC). The package has been thoroughly tested and applied to real data in the 
-  related research article entitled "Automatic Generation of Evolutionary 
-  Hypotheses using Mixed Gaussian Phylogenetic Models" (currently in review). 
+  related research article [@Mitov:2019agh] <doi:10.1073/pnas.1813823116>.
   Currently, the package is available from <https://github.com/venelin/PCMFit>. 
   The web-page <https://venelin.github.io/PCMFit/> provides access to 
   documentation and related resources.
@@ -68,6 +67,9 @@ module load interconnect/ethernet
 module load new gcc/6.3.0 open_mpi/1.6.5 r/3.4.0 intel/2017.5
 ```
 
+```{sh, eval = FALSE}
+xcode-select --install
+```
 Upon validating the availability of a C++ compiler, PCMBaseCpp can be installed using the commands:
 ```{r, eval=FALSE}
 # These two packages are available on CRAN
@@ -182,55 +184,16 @@ PCMFit builds on top of a stack of three tools enabling fast likelihood calculat
 
 # Citing PCMFit
 
-To acknowledge the PCMFit package in a publication or other presentation, please cite one of the following:
+To acknowledge the PCMFit package in a publication or other presentation, please cite:
 
-* Mitov, V. (2018). Phylogenetic Comparative Methods in the Era of Big Data, Doctoral Thesis No. [25428](https://doi.org/10.3929/ethz-b-000315296) ETH Zurich.
-```
-@phdthesis{Mitov:2018hh,
-author = {Mitov, Venelin},
-title = {{Phylogenetic Comparative Methods in the Era of Big Data}},
-school = {ETH Zurich},
-year = {2018},
-address = {Zurich},
-url = {https://doi.org/10.3929/ethz-b-000315296},
-}
-```
-Mitov, V., Bartoszek, K., & Stadler, T. (2018). Automatic Generation of Evolutionary Hypotheses using Mixed Gaussian Phylogenetic Models. Article in Review, preprint available in Chapter 7, Doctoral Thesis No. [25428](https://doi.org/10.3929/ethz-b-000315296) ETH Zurich.
+Mitov, V., Bartoszek, K., & Stadler, T. (2019). Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models. Proceedings of the National Academy of Sciences of the United States of America, http://doi.org/10.1073/pnas.1813823116.
 ```
 @article{Mitov:2019agh,
-author = {Mitov, Venelin and Bartoszek, Krzysztof and Stadler, Tanja},
-title = {{Automatic Generation of Evolutionary Hypotheses using Mixed Gaussian Phylogenetic Models}},
-journal = {article in review, preprint in Ch. 7 of Thesis No. 25428, ETH Zurich},
-year = {2019},
-pages = {},
-month = mai,
-url = {https://doi.org/10.3929/ethz-b-000315296}
-}
-```
-* Mitov, V., Bartoszek, K., Asimomitis, G., & Stadler, T. (2018, September 24). Fast likelihood evaluation for multivariate phylogenetic comparative methods: the PCMBase R package. arXiv.org. https://arxiv.org/abs/1809.09014.
-```
-@article{Mitov:2018fl,
-author = {Mitov, Venelin and Bartoszek, Krzysztof and Asimomitis, Georgios and Stadler, Tanja},
-title = {{Fast likelihood evaluation for multivariate phylogenetic comparative methods: the PCMBase R package}},
-journal = {arXiv.org},
-year = {2018},
-eprint = {1809.09014},
-eprinttype = {arxiv},
-eprintclass = {q-bio.PE},
-pages = {arXiv:1809.09014},
-month = sep,
-annote = {34 pages, 6 figures}
-}
-```
-* 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/10.1101/235739
-```
-@article{Mitov:2018dqa,
-author = {Mitov, Venelin and Stadler, Tanja},
-title = {{Parallel likelihood calculation for phylogenetic comparative models: The SPLITT C++ library}},
-journal = {Methods in Ecology and Evolution},
-year = {2018},
-pages = {2041--210X.13136},
-month = dec
+title = {Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models},
+    author = {Venelin Mitov and Krzysztof Bartoszek and Tanja Stadler},
+    journal = {Proceedings of the National Academy of Sciences of the United States of America},
+    year = {2019},
+    url = {https://www.pnas.org/lookup/doi/10.1073/pnas.1813823116},
 }
 ```