Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. 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 and then applies tidy data principles.

Turn complex 'JSON' data into tidy data frames.

Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package.

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.

Provides a unified tidyverse-compatible interface to R's machine
    learning packages. Wraps established implementations from 'glmnet',
    'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster',
    'dbscan', and others - providing consistent function signatures, tidy tibble
    output, and unified 'ggplot2'-based visualization. The underlying algorithms
    are unchanged; 'tidylearn' simply makes them easier to use together. Access
    raw model objects via the $fit slot for package-specific functionality.
    Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>,
    LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
    support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>,
    and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.