An implementation of a taxonomy of models of restricted diffusion
    in biological tissues parametrized by the tissue geometry (axis, diameter,
    density, etc.). This is primarily used in the context of diffusion magnetic
    resonance (MR) imaging to model the MR signal attenuation in the presence of
    diffusion gradients. The goal is to provide tools to simulate the MR signal
    attenuation predicted by these models under different experimental
    conditions. The package feeds a companion 'shiny' app available at
    <https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical
    interface to the models and tools provided by the package. Models currently
    available are the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van
    Gelderen et al. (1994) <doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997)
    <doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995)
    <doi:10.1006/jmra.1995.0014> and Callaghan (1995)
    <doi:10.1006/jmra.1995.1055>.

Implementation of the mid-n algorithms presented in 
             Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact 
             sample size calculation for superiority trials with binary outcome.

A guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. 'midoc' will advise which analysis approaches can be used, and how best to perform them. 'midoc' follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.

The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.