Provides a set of tools designed to enhance transparency and understanding of date-time manipulation functions from the 'lubridate' package. 
        It provides detailed feedback about the operations performed by 'lubridate' functions, allowing users to better comprehend and debug their code. 
        These insights serve as both a learning tool for newcomers and a debugging aid for programmers working with date-time data.

Reproduces the harmonized DB of the ESTAT survey of the same name. The survey data is served as separate spreadsheets with noticeable differences in the collected attributes. The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect.

Print vectors (and data frames) of floating point numbers
    using a non-scientific format optimized for human readers.  Vectors of
    numbers are rounded using significant digits, aligned at the decimal
    point, and all zeros trailing the decimal point are dropped.  See:
    Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point
    Numbers. In JSM Proceedings, Statistical Computing Section.
    Alexandria, VA: American Statistical Association. 2270-2279.

A lightweight wrapper for the 'Lucide' icon library
    <https://lucide.dev>. Embed beautiful and consistent 'SVG' icons
    inline in any 'R' web application.

Implements Latent Unknown Clusters By Integrating Multi-omics Data
    (LUCID; Peng (2019) <doi:10.1093/bioinformatics/btz667>) for integrative
    clustering with exposures, multi-omics data, and health outcomes.
    Supports three integration strategies: early, parallel, and serial.
    Provides model fitting and tuning, lasso-type regularization for exposure
    and omics feature selection, handling of missing data, including both
    sporadic and complete-case patterns, prediction, and g-computation for
    estimating causal effects of exposures, bootstrap inference for
    uncertainty estimation, and S3 summary and plot methods. For the
    multi-omics integration framework, see Jia (2024)
    <https://journal.r-project.org/articles/RJ-2024-012/RJ-2024-012.pdf>.
    For the missing-data imputation mechanism, see Jia (2024)
    <doi:10.1093/bioadv/vbae123>.