Title: Quotient of Random Variables Conditioned to the Positive
Description: Computes the exact probability density function of X/Y conditioned on positive quadrant for series of  bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.
Author: Yuancheng Si [aut, cre] (<https://orcid.org/0000-0003-0944-0013>),

Title: API Wrapper Around 'Postcodes.io'
Description: Free UK geocoding using data from Office for National Statistics.
It is using several functions to get information about post codes, outward
codes, reverse geocoding, nearest post codes/outward codes, validation, or
randomly generate a post code. API wrapper around <https://postcodes.io>.
Author: Eryk Walczak [aut, cre] (<https://orcid.org/0000-0001-8724-462X>),

Suggests: knitr, dplyr, rmarkdown, testthat, covr, purrr

Title: Non-Parametric Sampling with Parallel Monte Carlo
person(given = "Simon", family = "Lyddon", role = c("aut"), email = "simon.lyddon@stats.ox.ac.uk"),
person(given = "Miguel", family = "Morin", role = c("aut"), email = "info@turing.ac.uk"),
person(given = "James", family = "Robinson", role = c("aut", "cre"), email = "james.em.robinson@gmail.com"),
person("The Alan Turing Institute", role=c("cph"), email = "info@turing.ac.uk"))
Description: An implementation of a non-parametric statistical model using a
parallelised Monte Carlo sampling scheme. The method implemented in this
package allows non-parametric inference to be regularized for small sample
sizes, while also being more accurate than approximations such as
variational Bayes. The concentration parameter is an effective sample size
parameter, determining the faith we have in the model versus the data. When
the concentration is low, the samples are close to the exact Bayesian
logistic regression method; when the concentration is high, the samples are
close to the simplified variational Bayes logistic regression. The method is
described in full in the paper Lyddon, Walker, and Holmes (2018),
"Nonparametric learning from Bayesian models with randomized objective
Packaged: 2019-10-09 10:13:58 UTC; jrobinson