Provides tools for calculating evolvability parameters from estimated G-matrices as defined 
    in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic 
    comparative models that link the rate of evolution of a trait to the state of another evolving 
    trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was 
    released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.

The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible 
      package providing optimization driven parameter estimation methods using metaheuristics  and 
      evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization 
      for continuous domains, Tabu Search, Evolutionary Strategies, ...)  which could be more efficient and require, 
      in some cases, fewer model evaluations than alternatives relying on experimental design.  Currently there 
      are built in support for models developed with 'Repast Simphony'  Agent-Based framework (<https://repast.github.io/>)  
      and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.

Performs automated morphological character partitioning for 
             phylogenetic analyses and analyze macroevolutionary parameter 
             outputs from clock (time-calibrated) Bayesian inference analyses, following 
             concepts introduced by Simões and Pierce (2021) <doi:10.1038/s41559-021-01532-x>.

Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.