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 (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.

A reproducible, tidyverse-style framework for intensive longitudinal
  data analysis in R, with built-in methodological safeguards, provenance
  tracking, and reporting tools. Encodes time structure, enforces
  within-between decomposition, provides spacing-aware lags, and integrates
  diagnostics and visualization. Use ild_prepare(), ild_center(), ild_lag(),
  and related functions for a unified pipeline from raw EMA/diary data to
  interpretable models.

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.

Provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII
    Standard) well log files into tidy, analysis-ready tabular formats. The
    package separates LAS header information and log data into structured
    components, builds a searchable index across collections of LAS files,
    and enables reproducible subsetting of wells based on metadata or curve
    availability. Output tables can be written to CSV or Parquet formats to
    support large-scale statistical, machine learning, and earth science
    workflows. The tidy data structure follows Wickham (2014) <doi:10.18637/jss.v059.i10>.
    The LAS file structure follows the Canadian Well Logging Society LAS standard
    <https://www.cwls.org/wp-content/uploads/2017/02/Las2_Update_Jan2017.pdf>.