Simulate, estimate and forecast a wide range of regression
    based dynamic models for bounded time series, covering the most
    commonly applied models in the literature. The main calculations are
    done in FORTRAN, which translates into very fast algorithms.

Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties.
  The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors.
  Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.

Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) <doi:10.48550/arXiv.2408.10558>, and allows for the statistical modeling of multi-attribute pairwise comparison data.

A complete toolkit for connecting 'R' environments with Large
    Language Models (LLMs). Provides utilities for describing 'R' objects,
    package documentation, and workspace state in plain text formats
    optimized for LLM consumption. Supports multiple workflows:
    interactive copy-paste to external chat interfaces, programmatic tool
    registration with 'ellmer' chat clients, batteries-included chat
    applications via 'shinychat', and exposure to external coding agents
    through the Model Context Protocol. Project configuration files enable
    stable, repeatable conversations with project-specific context and
    preferred LLM settings.

Implements the Butterworth-Induced Autoregressive ('BTWAR')
    model, where autoregressive coefficients are obtained from analog
    Butterworth filter prototypes mapped into the discrete-time domain
    using the Matched Z-Transform. The framework establishes a structured
    connection between frequency-domain filter design and time-domain
    autoregressive modeling. Model order selection is performed via
    nested rolling-origin cross-validation. Method described in
    Bras-Geraldes, Rocha and Martins (2026) <doi:10.3390/math14030479>.