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>.

Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.

Provides a function to calculate infinite-jackknife-based
    standard errors for fixed effects parameters in 'brms' models, handling
    both clustered and independent data. 
    References: Ji et al. (2024) <doi:10.48550/arXiv.2407.09772>; Giordano et al. (2024) <doi:10.48550/arXiv.2305.06466>.

General purpose TIFF file I/O for R users.  Currently the
    only such package with read and write support for TIFF files with
    floating point (real-numbered) pixels, and the only package that can
    correctly import TIFF files that were saved from 'ImageJ' and write
    TIFF files than can be correctly read by 'ImageJ'
    <https://imagej.net/ij/>. Also supports text image I/O.

Simple handling of survey data. Smart handling of meta-information like e.g. variable-labels value-labels and scale-levels. Easy access and validation of meta-information. Useage of value labels and values respectively for subsetting and recoding data.