The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
             comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
             The estimation is based on latent copula Gaussian models.
             For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017).
             For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <doi:10.48550/arXiv.1809.06255>.
             For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
             For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.

Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.

Executes arbitrary R or C functions some time after the
    current time, after the R execution stack has emptied. The functions
    are scheduled in an event loop.

Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.

Implements the Linear Approach to Threshold with Ergodic Rate 
    (LATER) model, which predicts distributions of reaction times and 
    summarises them with as little as two free parameters. Allows for easy 
    visualisation and comparison of datasets, along with fitting of datasets 
    using the LATER model.