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.

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>.

Provides computational methods for detecting adverse high-order drug interactions
         from individual case safety reports using statistical techniques,
         allowing the exploration of higher-order interactions among drug cocktails.

EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.

For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).