Create browsers for reading full texts from a token list format.
    Information obtained from text analyses (e.g., topic modeling, word scaling)
    can be used to annotate the texts.

Convert natural language text into tokens. Includes tokenizers for
    shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs,
    characters, shingled characters, lines, Penn Treebank, regular
    expressions, as well as functions for counting characters, words, and sentences,
    and a function for splitting longer texts into separate documents, each with
    the same number of words.  The tokenizers have a consistent interface, and
    the package is built on the 'stringi' and 'Rcpp' packages for  fast
    yet correct tokenization in 'UTF-8'.

Unsupervised text tokenizer focused on computational efficiency. Wraps the 'YouTokenToMe' library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.

Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.

Email Finder R Client Library.
        Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet.
        Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name.
        Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.