Implements the Model Context Protocol (MCP). Users can start
    'R'-based servers, serving functions as tools for large language
    models to call before responding to the user in MCP-compatible apps
    like 'Claude Desktop' and 'Claude Code', with options to run those
    tools inside of interactive 'R' sessions. On the other end, when 'R'
    is the client via the 'ellmer' package, users can register tools from
    third-party MCP servers to integrate additional context into chats.

A unified toolkit for classical test theory (CTT) item analysis
    of multiple-choice test data, including item difficulty (p-value),
    item discrimination (point-biserial correlation and upper-lower
    27-percent discrimination index), per-distractor analysis (frequency,
    proportion, and discrimination), and Haladyna's distractor efficiency.
    A wrapper function returns a tidy 'mcq_analysis' object with print,
    plot (difficulty-discrimination scatter), and APA-style table methods
    for direct inclusion in journal manuscripts. Implemented in pure R with
    no compiled code and minimal dependencies.

Regression methods to quantify the relation between two measurement
    methods are provided by this package. In particular it addresses regression
    problems with errors in both variables and without repeated measurements. It
    implements the CLSI recommendations (see J. A. Budd et al. 
    (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) 
    for analytical method comparison and bias estimation using patient samples.
    Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators 
    are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and 
    J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>).
    A comprehensive overview over the implemented methods and references can be found 
    in the manual pages "mcr-package" and "mcreg".

Provides methods and functions to analyze the quantitative or 
    qualitative performance for diagnostic assays, and outliers detection, 
    reader precision and reference range are discussed. Most of the methods 
    and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) 
    recommendations and NMPA (National Medical Products Administration) 
    guidelines. In additional, relevant plots are constructed by 'ggplot2'.