Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.

Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors regression model to each terminal node.

Provides advanced Bayesian methods to estimate
	     abundance and run-timing from temporally-stratified
	     Petersen mark-recapture experiments. Methods include
	     hierarchical modelling of the capture probabilities
  	     and spline smoothing of the daily run size. Theory
  	     described in Bonner and Schwarz (2011)
         <doi:10.1111/j.1541-0420.2011.01599.x>.

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.