An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).

Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.

Tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in 'RStudio' or 'Linux' environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the 'foreach' package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code.

Performs the execution of the main procedures of multiple 
    comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, 
    Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations 
    and export to different extensions of its results. An additional 
    part of the package is the presence of the performance evaluation 
    of the tests (Type I error per experiment and the power). This will 
    assist the user in making the decision for the chosen test.

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.

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".