A declarative language for specifying multilevel models,
    solving for population parameters based on specified variance-explained effect
    size measures, generating data, and conducting power analyses to determine
    sample size recommendations. The specification allows for any number of
    within-cluster effects, between-cluster effects, covariate effects
    at either level, and random coefficients. Moreover, the models do not
    assume orthogonal effects, and predictors can correlate at either level
    and accommodate models with multiple interaction effects.

Data and examples from a multilevel modelling software review
  as well as other well-known data sets from the multilevel modelling
  literature.

Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.

An implementation of several machine learning algorithms for 
    multivariate time series. The package includes functions allowing the
    execution of clustering, classification or outlier detection methods,
    among others. It also incorporates a collection of multivariate time
    series datasets which can be used to analyse the performance of new
    proposed algorithms. Some of these datasets are stored in GitHub data
    packages 'ueadata1' to 'ueadata8'. To access these data packages, run
    'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='<https://anloor7.github.io/drat/>')'.
    The installation takes a couple of minutes but we strongly encourage the
    users to do it if they want to have available all datasets of mlmts.
    Practitioners from a broad variety of fields could
    benefit from the general framework provided by 'mlmts'.