Jean-Luc Lipatz [aut, cre], Bernard Gestin [ctb]
shiny, shinydashboard, shinyjs, shinyWidgets, shinyFiles,
htmltools, rhandsontable, sortable, purrr, stringr, glue,
lubridate, zoo, tibble, dplyr (>= 1.0.0), tidyr, fuzzyjoin,
rio, jsonlite, fst, feather, haven, readxl, Hmisc, skimr,
tables, ggformula, mapsf, clipr, magrittr

shiny, shinydashboard, shinyWidgets, shinyFiles, htmltools,
rhandsontable, sortable, purrr, stringr, glue, lubridate, zoo,
tibble, dplyr (>= 1.0.0), tidyr, fuzzyjoin, rio, jsonlite, fst,
feather, haven, readxl, readODS, Hmisc, skimr, tables,
ggformula, mapsf, clipr, magrittr

Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (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.

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.

Analyzing Data Through of Percentage of Importance Indice
(Production Unknown) and Its Derivations
c(person(given = "Germano Leao",
family = "Demolin-Leite",
role = "aut",
email = "germano.demolin@gmail.com",
comment = c(ORCID = "0000-0002-2928-3193")),
person(given = "Alcinei Mistico",
family = "Azevedo",
role = c("aut", "cre"),
email = "alcineimistico@hotmail.com",
comment = c(ORCID = "0000-0001-5196-0851")))
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>.
Germano Leao Demolin-Leite [aut]
(<https://orcid.org/0000-0002-2928-3193>),
Alcinei Mistico Azevedo [aut, cre]
(<https://orcid.org/0000-0001-5196-0851>)