Create PX-files from scratch or read and modify existing ones. 
    Includes a function for every PX keyword, making metadata manipulation
    simple and human-readable.

Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.

Generic interface for the PX-Web/PC-Axis API. The
    PX-Web/PC-Axis API is used by organizations such as Statistics Sweden
    and Statistics Finland to disseminate data. The R package can interact
    with all PX-Web/PC-Axis APIs to fetch information about the data
    hierarchy, extract metadata and extract and parse statistics to R
    data.frame format. PX-Web is a solution to disseminate PC-Axis data
    files in dynamic tables on the web.  Since 2013 PX-Web contains an API
    to disseminate PC-Axis files.

Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.

Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame. 
 Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.

Provides tools for pycnophylactic interpolation of polygon totals to discrete global and local grid systems. The method follows Tobler (1979) <doi:10.1080/01621459.1979.10481647>, preserving source-zone totals while smoothing values across neighboring target cells.

Implements the Penalized Youden Index Estimator (PYE) and the Covariate-Adjusted Youden Index Estimator (covYI), providing a novel framework for feature and covariate selection and combination in high-dimensional binary classification problems. Methodologies are based on Salaroli and Pardo (2023) <doi:10.1016/j.chemolab.2023.104786> and an unpublished manuscript by Salaroli and Pardo (2026) under review.

Deterministic Pena-Yohai initial estimator for robust S estimators
    of regression. The procedure is described in detail in
    Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.