Title: Maximum Likelihood and Bayesian Estimation of Univariate
Description: This package contains a series of functions that might be
useful in carrying out maximum likelihood and Bayesian
estimations of univariate probability distributions

Description: A series of functions that might be useful in carrying out
maximum likelihood and Bayesian estimations of univariate probability distributions

Description: Estimate parameters of univariate probability distributions
with maximum likelihood and Bayesian methods.

Title: Functions and Datasets for "Forest Analytics with R".
Description: This package provides functions and datasets from the book

Title: Functions and Datasets for "Forest Analytics with R"
Depends: R (>= 2.10), MASS, lattice, glpkAPI
Description: Provides functions and datasets from the book "Forest Analytics with R".

Title: Phase I Optimal Dose Assignment using the FBCRM and MFBCRM
Authors@R: c(person("Soham", "Mahato", role = c("aut"),
person("Andrew G", "Chapple", role = c("aut","cre"),
Description: 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.

Title: Algorithm for searching the space of Gaussian directed acyclic
graphical models through moment fractional Bayes factors
Author: Davide Altomare, Guido Consonni and Luca La Rocca
Description: 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.
Depends: Rcpp (>= 0.9.13), RcppArmadillo (>= 0.3.2.4)