A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution 
    of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. 
    It is based on a spatial geometry for describing the landscape and allocation of different cultivars, 
    a dispersal kernel for the dissemination of the pathogen, and a SEIR 
    ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step.
    It provides a useful tool to assess the performance of a wide range of deployment options with 
    respect to their epidemiological, evolutionary and economic outcomes.
    Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
    Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.

Translates R help documentation on the fly by using a Large
    Language model of your choice. If you are using 'RStudio' or
    'Positron' the translated help will appear in the help pane.

Estimate drift and diffusion functions from time series and
    generate synthetic time series from given drift and diffusion coefficients.

Provides lightweight, dependency-minimal implementations of
    Langevin diffusion based Markov chain Monte Carlo samplers, including
    the Unadjusted Langevin Algorithm (ULA) and the Metropolis-Adjusted
    Langevin Algorithm (MALA). The core sampling loops are written in C++
    via 'Rcpp' and 'RcppArmadillo' for performance, while exposing a simple
    R-level interface where the user supplies the gradient of the negative
    log-density (and, for MALA, the negative log-density itself). Intended
    as a building block for Bayesian inference and stochastic optimization
    rather than a full probabilistic programming framework. Methods follow
    Roberts and Tweedie (1996) <doi:10.2307/3318418> and Roberts and
    Rosenthal (1998) <doi:10.1111/1467-9868.00123>.

An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.