A consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.

Functions to design and apply tests that are anytime valid. The
  functions can be used to design hypothesis tests in the prospective/randomised
  control trial setting or in the observational/retrospective setting. The
  resulting tests remain valid under both optional stopping and optional
  continuation. The current version includes safe t-tests and safe tests of
  two proportions. For details on the theory of safe tests, see
  Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, 
  for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald
  (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with 
  Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021)
  "Safe Tests and Always-Valid Confidence Intervals for contingency tables and 
  beyond" <arXiv:2106.02693> for details on safe contingency table tests.

A file format for storing tensors that is secure (doesn't allow for
    code execution), fast and simple to implement. 'safetensors' also enables cross
    language and cross frameworks compatibility making it an ideal format for
    storing machine learning model weights.

Contains chart code for monitoring clinical trial safety. Charts can be used as standalone output, but are also designed for use with the 'safetyGraphics' package, which makes it easy to load data and customize the charts using an interactive web-based interface created with Shiny.

Example clinical trial data sets formatted for easy use in R.

A framework for evaluation of clinical trial safety. Users can interactively explore their data using the included 'Shiny' application.

Fork of 'vote_2.3-2', Raftery et al. (2021)
  <DOI:10.32614/RJ-2021-086>, with additional support 
  for stochastic experimentation.

Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). 
    The first chapter of the book is dedicated to an introduction to statistics and their world. 
    The second chapter deals with univariate exploratory statistics and graphics. 
    The third chapter deals with bivariate and multivariate exploratory statistics and graphics. 
    The fourth chapter is dedicated to data exploration with Principal Component Analysis. 
    The fifth chapter is dedicated to data exploration with Correspondance Analysis.
    The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. 
    The seventh chapter is dedicated to data exploration with automatic clustering. 
    The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions.
    The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests.
    The tenth chapter is dedicated to an Gaussian linear model.
    The eleventh chapter is dedicated to an introduction to time series.
    The twelfth chapter is dedicated to an introduction to probit and logit models.
    Various example datasets are shipped with the package as well as some new functions.