The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful 'pheatmap' package, it allows for the effortless creation of intricate heatmaps with minimal code.

Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (n.d.) <arXiv:2004.14497>. You provide a simple
    recipe for what machine learning algorithms to use in estimating the nuisance
    functions and 'tidyhte' will take care of cross-validation, estimation, model
    selection, diagnostics and construction of relevant quantities of interest about
    the variability of treatment effects.

Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and
    <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.

Construct and analyse indexes in a pipeline tidy workflow.
    'tidyindex' contains modules for transforming variables, aggregating 
    variables across time, reducing data dimension through weighting, and 
    fitting distributions. A manuscript describing the methodology can be 
    found at <https://github.com/huizezhang-sherry/paper-tidyindex>.

Turn complex 'JSON' data into tidy data frames.

Implements an algorithm for Latent Dirichlet
    Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
    using style conventions from the 'tidyverse',
    Wickham et al. (2019)<doi:10.21105/joss.01686>,
    and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
    Fitting is done via collapsed Gibbs sampling.
    Also implements several novel features for LDA such as guided models and
    transfer learning based on ongoing and, as yet, unpublished research.

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.