Functions and Datasets for "Forest Analytics with R"
Provides functions and datasets from the book "Forest Analytics with R".

Phase I Optimal Dose Assignment using the FBCRM and MFBCRM
Methods
c(person("Soham", "Mahato", role = c("aut"),
email = "soham.iips@gmail.com"),
person("Andrew G", "Chapple", role = c("aut","cre"),
email="achapp@lsuhsc.edu") )
Performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. These trial designs extend the Continual Reassessment Method (CRM) and Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) by allowing the prior toxicity skeleton itself to be random, with posterior distributions obtained from Markov Chain Monte Carlo. On average, the FBCRM and MFBCRM methods outperformed the CRM and BMA-CRM methods in terms of selecting an optimal dose level across thousands of randomly generated simulation scenarios. Details on the methods and results of this simulation study are available on request, and the manuscript is currently under review.
Soham Mahato [aut],
Andrew G Chapple [aut, cre]

Algorithm for searching the space of Gaussian directed acyclic
graphical models through moment fractional Bayes factors
Davide Altomare, Guido Consonni and Luca La Rocca
We propose an objective Bayesian algorithm for searching
the space of Gaussian directed acyclic graphical models when
the variables are assumed to satisfy a given ordering. The
approach used is based on non-local parameter priors and thus
it is suitable for learning sparse graphs. The algorithm is
implemented in C++ using the open-source library Armadillo.

Algorithm for Searching the Space of Gaussian Directed Acyclic
Graph Models Through Moment Fractional Bayes Factors
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.