A collection of R Markdown templates for creating simple and easy 
  to personalize single page websites.

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>.

Generates simple and beautiful one-page HTML reference manuals 
    with package documentation. Math rendering and syntax highlighting are done 
    server-side in R such that no JavaScript libraries are needed in the
    browser, which makes the documentation portable and fast to load.

Provides useful tools for both users and developers of packages 
  for fitting Bayesian models or working with output from Bayesian models. 
  The primary goals of the package are to: 
  (a) Efficiently convert between many different useful formats of
  draws (samples) from posterior or prior distributions.
  (b) Provide consistent methods for operations commonly performed on draws, 
  for example, subsetting, binding, or mutating draws.
  (c) Provide various summaries of draws in convenient formats.
  (d) Provide lightweight implementations of state of the art posterior 
  inference diagnostics. References: Vehtari et al. (2021) 
  <doi:10.1214/20-BA1221>.

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
    functions" <arXiv:1806.11544>.