Title: Bayesian Network Learning with the PCHC Algorithm
Authors@R: c(person("Michail", "Tsagris", role = c("aut", "cre"), email = "mtsagris@uoc.gr"))
Imports: bnlearn, Rfast, robustbase, stats
Description: Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing. It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then utilizes the Hill-Climbing greedy search. The relevant paper has been submitted and is currently in revision.

Title: Bayesian Network Learning with the PCHC and Related Algorithms
Imports: bigmemory, bigstatsr, bnlearn, Rfast, Rfast2, robustbase,
Description: Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing. It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then utilizes the Hill-Climbing greedy search. The relevant paper is: Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics (Accepted for publication). <doi:10.1007/s10614-020-10065-7>.

Description: Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing.
It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then
utilizes the Hill-Climbing greedy search. The relevant papers are a) Tsagris M. (2021).
"A new scalable Bayesian network learning algorithm with applications to economics".
Computational Economics (To appear). <doi:10.1007/s10614-020-10065-7>.
b) Tsagris M. (2020). The FEDHC Bayesian network learning algorithm. <arXiv:2012.00113>.

Description: Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing. It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then
utilizes 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. The relevant papers are
a) Tsagris M. (2021). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>.