The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.

Integrating morphological modeling with machine learning to support
   structured decision-making (e.g., in management and consulting). The package
   enumerates a morphospace of feasible configurations and uses random forests
   to estimate class probabilities over that space, bridging deductive model
   exploration with empirical validation. It includes utilities for factorizing
   inputs, model training, morphospace construction, and an interactive 'shiny'
   app for scenario exploration.

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>.