Suggests: testthat (>= 2.0), label.switching (>= 1.7), readr (>=

Description: An implementation of the Bayesian version of the Mallows rank model
(Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>;
Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). Both Cayley, footrule,
Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
posterior distributions of parameters are provided. The package also provides functions for estimating
the partition function (normalizing constant) of the Mallows rank model, both with the importance
sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
(Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Suggests: R.rsp, testthat (>= 2.0), label.switching (>= 1.7), readr (>=

(Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;

Author: Oystein Sorensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>),

Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). Both Metropolis-Hastings
and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
Suggests: R.rsp, knitr, testthat (>= 3.0.0), label.switching (>= 1.7),

0.3.1), PerMallows (>= 1.13), HDInterval (>= 0.2.0), cowplot
Suggests: R.rsp, xml2, knitr, testthat (>= 3.0.0), label.switching (>=
1.7), rmarkdown, covr, parallel (>= 3.5.1)

Title: Bayesian Data Reconciliation of Separation Processes
Description: Bayesian tools that can be used to reconcile, or mass balance, mass flow rate data collected from chemical or particulate separation processes aided by constraints governed by the conservation of mass.
Functions included in the package aid the user in organizing and constraining data, using Markov chain Monte Carlo methods to obtain samples from Bayesian models, and in computation of the marginal likelihood of the data, given a particular model, for model selection.  Marginal likelihood is approximated by methods in Chib S (1995) <doi:10.2307/2291521>.
Imports: Rdpack, Matrix, MASS, pracma, tmvtnorm, LaplacesDemon,
Suggests: knitr, rmarkdown, covr, spelling, tgp, testthat