Web scraping the <https://www.dallasfed.org> for
    up-to-date data on international house prices and exuberance
    indicators. Download data in tidy format.

Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.

An offline suite of tools to clean, aggregate, and harmonise data
    from the 'Malawi Integrated Household Survey' ('IHS'), covering rounds two
    through six (2004 to 2025). Provides crop-specific unit conversions,
    stratified winsorization, consumer-price deflation, and automatic
    cross-round harmonisation for complex survey designs.

A novel machine learning method for plant viruses diagnostic using 
    genome sequencing data. This package includes three different machine 
    learning models, random forest, XGBoost, and elastic net, to train and 
    predict mapped genome samples. Mappability profile and unreliable regions 
    are introduced to the algorithm, and users can build a mappability profile 
    from scratch with functions included in the package. Plotting mapped sample 
    coverage information is provided.

Computes individual contributions to the overall Gini and 
    Theil's T and Theil's L measures and their decompositions by groups 
    such as race, gender, national origin, with the three functions of 
    iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019)
    <doi:10.1177/0049124119875961>.

The Importance Index (I.I.) can determine the loss and solution sources for a system in certain knowledge areas (e.g., agronomy), when production (e.g., fruits) is known (Demolin-Leite, 2021). Events (e.g., agricultural pest) can have different magnitudes (numerical measurements), frequencies, and distributions (aggregate, random, or regular) of event occurrence, and I.I. bases in this triplet (Demolin-Leite, 2021) <https://cjascience.com/index.php/CJAS/article/view/1009/1319>. Usually, the higher the magnitude and frequency of aggregated distribution, the greater the problem or the solution (e.g., natural enemies versus pests) for the system (Demolin-Leite, 2021). However, the final production of the system is not always known or is difficult to determine (e.g., degraded area recovery). A derivation of the I.I. is the percentage of Importance Index-Production Unknown (% I.I.-PU) that can detect the loss or solution sources, when production is unknown for the system (Demolin-Leite, 2024) <DOI:10.1590/1519-6984.253218>.