Extends the 'caret' framework to support late fusion workflows,
    enabling users to train models independently across multiple data modalities and combine their
    predictions into a single meta-model. Designed for developers, data scientists, and 
    biomedical researchers alike, 'caretMultimodal' aims to make late fusion ensemble modelling
    as accessible and flexible as single-dataset workflows in 'caret'. Late fusion methods are based on Wolpert (1992) 
    <doi:10.1016/S0893-6080(05)80023-1>.

Use machine learning algorithms and advanced geographic information system tools to
    build Species Distribution Modeling in a extensible and modern fashion.

We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.

A framework is provided to develop R packages using 'Rust' <https://www.rust-lang.org/> with
 minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' <https://doc.rust-lang.org/cargo/> in a manner
 consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with,
 nor endorsed by the Rust project.