Maximum likelihood estimation under relational models, with or without the overall effect.

The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean
    and the standard deviation of a quantitative variable. It also flags 
    potential outliers (together with their frequencies) that are c standard deviations away from the mean.

Find the permutation symmetry group such that the covariance
    matrix of the given data is approximately invariant under it.
    Discovering such a permutation decreases the number of observations
    needed to fit a Gaussian model, which is of great use when it is
    smaller than the number of variables. Even if that is not the case,
    the covariance matrix found with 'gips' approximates the actual
    covariance with less statistical error. The methods implemented in
    this package are described in Graczyk et al. (2022)
    <doi:10.1214/22-AOS2174>. Documentation about 'gips' is
    provided via its website at <https://przechoj.github.io/gips/> and
    the paper by Chojecki, Morgen, Kołodziejek
    (2025, <doi:10.18637/jss.v112.i07>).

Extends classical linear and quadratic discriminant analysis
    by incorporating permutation group symmetries into covariance matrix
    estimation. The package leverages methodology from the 'gips'
    framework to identify and impose permutation structures that act as a
    form of regularization, improving stability and interpretability in
    settings with symmetric or exchangeable features. Several discriminant
    analysis variants are provided, including pooled and class-specific
    covariance models, as well as multi-class extensions with shared or
    independent symmetry structures. For more details about 'gips' methodology see
    and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen,
    Kołodziejek (2025, <doi:10.18637/jss.v112.i07>).

Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices.
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the
 likelihood using MCMC samplers for large lattices.