How can we measure how the usage or frequency of some
    feature, such as words, differs across some group or set, such as
    documents? One option is to use the log odds ratio, but the log odds
    ratio alone does not account for sampling variability; we haven't
    counted every feature the same number of times so how do we know which
    differences are meaningful? Enter the weighted log odds, which
    'tidylo' provides an implementation for, using tidy data principles.
    In particular, here we use the method outlined in Monroe, Colaresi,
    and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds ratio
    by a prior. By default, the prior is estimated from the data itself,
    an empirical Bayes approach, but an uninformative prior is also
    available.

Provides feedback about 'dplyr' and 'tidyr' operations.

Easily carry out latent profile analysis ("LPA"), determine the
    correct number of classes based on best practices, and tabulate and plot the
    results. Provides functionality to estimate commonly-specified models with
    free means, variances, and covariances for each profile. Follows a tidy
    approach, in that output is in the form of a data frame that can
    subsequently be computed on. Models can be estimated using the free open
    source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program
    'MPlus', via the 'MplusAutomation' package.

Framework to run Monte Carlo simulations over a parameter grid. 
             Allows to parallelize the simulations. 
             Generates plots and 'LaTeX' tables
             summarizing the results from the simulation.

The tidy modeling "verse" is a collection of packages for
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.

Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.