Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").

Predictors can be converted to one or more numeric
    representations using a variety of methods. Effect encodings using
    simple generalized linear models <doi:10.48550/arXiv.1611.09477> or
    nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There
    are also functions for dimension reduction and other approaches.

Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.

Calculate and analyze household energy burden using the Net Energy
    Return aggregation methodology. Functions support weighted statistical
    calculations across geographic and demographic cohorts, with utilities for
    formatting results into publication-ready tables. Methods are based on
    Scheier & Kittner (2022) <doi:10.1038/s41467-021-27673-y>.

Fit Bayesian (hierarchical) cognitive models
    using a linear modeling language interface using particle Metropolis Markov
    chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), 
    linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal
    race model (LNR) are supported. Additionally, users can specify their own likelihood
    function and/or choose for non-hierarchical
    estimation, as well as for a diagonal, blocked or full multivariate normal
    group-level distribution to test individual differences. Prior specification 
    is facilitated through methods that visualize the (implied) prior. 
    A wide range of plotting functions assist in assessing model convergence and
    posterior inference. Models can be easily evaluated using functions
    that plot posterior predictions or using relative model comparison metrics 
    such as information criteria or Bayes factors.
    References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.