Title: Analyzing Data Through of Percentage of Importance Indice
(Production Unknown) and Its Derivations
Description: 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>.
Author: Germano Leao Demolin-Leite [aut]

Packaged: 2022-10-11 22:57:07 UTC; AlcineiAzevedo

Title: Datasets to Accompany Wolfe and Schneider - Intuitive
Description: These datasets and functions accompany Wolfe and Schneider (2017) - Intuitive Introductory Statistics (ISBN:  978-3-319-56070-0) <http://www.springer.com/us/book/9783319560700>. They are used in the examples throughout the text and in the end-of-chapter exercises. The datasets are meant to cover a broad range of topics in order to appeal to the diverse set of interests and backgrounds typically present in an introductory Statistics class.

Title: Modeling Within Individual Variability as Predictor
Authors@R: c(person(given="Lijuan", family="Wang", role = c("aut","cph")), person(given="Xiao", family="Liu", role = c("aut", "cre","cph"), email = "xliu19@nd.edu"), person(given="Miao", family="Yang", role = c("aut","cph")))
Description: 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>.