The goal of 'siteymlgen' is to make it easy to organise
  the building of your 'R Markdown' website.
  The init() function placed within the first code chunk of the index.Rmd
  file of an 'R' project directory will initiate the generation of an automatically
  written _site.yml file. 'siteymlgen' recommends a specific naming
  convention for your 'R Markdown' files. This naming will ensure that
  your navbar layout is ordered according to a hierarchy.

Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.

Provided within are two high quality and fast PPRNGs that may be
    used in an 'OpenMP' parallel environment. In addition, there is a generator
    for one dimensional low-discrepancy sequence. The objective of this library
    to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and
    'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the
    header files inside of 'sitmo' instead of including a copy of each engine
    within their individual package. Lastly, the package contains example
    implementations using the 'sitmo' package and three accompanying vignette that
    provide additional information.

Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2

Framework to build an individual tree simulator.

Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.

An end-to-end toolkit for land use and land cover classification
    using big Earth observation data, based on machine learning methods 
    applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>.
    Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, 
    Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, 
    NASA HLS using the Spatio-temporal Asset Catalog (STAC) 
    protocol (<https://stacspec.org/>) and the 'gdalcubes' R package 
    developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>.
    Supports visualization methods for images and time series and 
    smoothing filters for dealing with noisy time series.
    Includes functions for quality assessment of training samples using self-organized maps 
    as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. 
    Provides machine learning methods including support vector machines, 
    random forests, extreme gradient boosting, multi-layer perceptrons,
    temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
    and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>.
    Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>.
    Performs efficient classification of big Earth observation data cubes and includes 
    functions for post-classification smoothing based on Bayesian inference, and 
    methods for active learning and uncertainty assessment. Supports object-based 
    time series analysis using package supercells <https://jakubnowosad.com/supercells/>.
    Enables best practices for estimating area and assessing accuracy of land change as 
    recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>.
    Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.