Imports: bigstatsr, bnlearn, Rfast, Rfast2, robustbase, stats
Description: Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then
applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU.
b) Tsagris M. (2021). The FEDHC Bayesian network learning algorithm. <arXiv:2012.00113>.

Imports: bigstatsr, bnlearn, dcov, Rfast, Rfast2, robustbase, stats
a) Tsagris M. (2022). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>.

Title: Approximate Bayesian Computations for Phylogenetic Comparative
Author: Krzysztof Bartoszek <krzbar@protonmail.ch>, Pietro Lio'
Description: Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2018) <arXiv:1812.03715>.
Imports: ape (>= 3.0.6), distory, geiger, graphics, methods, mvSLOUCH,
phangorn, stats, TreeSim, treespace, utils, yuima
Collate: ABCdist.R ABCmessage.R ABC.R ABCupdate.R draw.R getTipSample.R

Description: Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) <10.5506/APhysPolBSupp.12.25>.

Author: Krzysztof Bartoszek <bartoszekkj@gmail.com>, Pietro Lio'
Description: Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data.