Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.

Data screening is an important first step of any statistical
    analysis. dataMaid auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset.

Designed to create a basic data dictionary and append to the original dataset's attributes list. The package makes use of a tidy dataset and creates a data frame that will serve as a linker that will aid in building the dictionary. The dictionary is then appended to the list of the original dataset's attributes. The user will have the option of entering variable and item descriptions by writing code or use alternate functions that will prompt the user to add these.

Set of tools aimed at processing meteorological data, converting hourly recorded data to daily, monthly and annual data.

'Shiny' modules to import data into an application or 'addin'
    from various sources, and to manipulate them after that.

A grammar of data manipulation with 'data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.

Datasets and Functions to Accompany Salas-Eljatib (2021, ISBN: 9789566086109) 
    "Analisis de datos con el programa estadistico R: una introduccion aplicada". The package helps carry out data management, exploratory analyses, and model fitting.

Creating, and refining data nuggets. 
    Data nuggets reduce a large dataset into a small collection of nuggets of 
    data, each containing a center (location), weight (importance), and scale 
    (variability) parameter. Data nugget centers are created by choosing 
    observations in the dataset which are as equally spaced apart as possible. 
    Data nugget weights are created by counting the number observations 
    closest to a given data nugget’s center. We then say the data nugget 
    'contains' these observations and the data nugget center is recalculated 
    as the mean of these observations. Data nugget scales are created by 
    calculating the trace of the covariance matrix of the observations 
    contained within a data nugget divided by the dimension of the dataset. 
    Data nuggets are refined by 'splitting' data nuggets which have scales or 
    shapes (defined as the ratio of the two largest eigenvalues of the 
    covariance matrix of the observations contained within the data nugget) 
    Reference paper: [1] Cherasia, K. E., Cabrera, J., Fernholz, L. T., & Fernholz, R. (2022). Data Nuggets in Supervised Learning. \emph{In Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler} (pp. 429-449). Cham: Springer International Publishing. [2] Beavers, T., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., Teigler, J. (2023). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure (Submitted for Publication).