person("Jose", "Storopoli", role = c("aut", "cre"), email = "jstoropoli@protonmail.com", comment = c(ORCID = "0000-0002-0559-5176"))

Factor Copula Models for Mixed Continuous and Discrete Data
c(person("Sayed H.", "Kadhem", role = c("aut","cre"), email = "s.kadhem@uea.ac.uk"), person("Aristidis K.", "Nikoloulopoulos", role = c("aut"), email = "a.nikoloulopoulos@uea.ac.uk"))
Sayed H. Kadhem [aut, cre],
Aristidis K. Nikoloulopoulos [aut]
R (>= 3.5.0), statmod, abind, utils, polycor
Estimation, model selection and goodness-of-fit of factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2019) <arXiv:1907.07395>.

Factor, Bi-Factor and Second-Order Copula Models
R (>= 3.5.0), statmod, abind, utils, polycor, VineCopula,
matlab
Estimation, model selection and goodness-of-fit of (1) factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2021) <doi:10.1111/bmsp.12231>; (2) bi-factor and second-order copula models for item response data in Kadhem and Nikoloulopoulos (2021) <arXiv:2102.10660>.

Factor, Bi-Factor, Second-Order and Factor Tree Copula Models
c(person("Sayed H.", "Kadhem", role = c("aut")), person("Aristidis K.", "Nikoloulopoulos", role = c("aut","cre"), email = "a.nikoloulopoulos@uea.ac.uk"))
Sayed H. Kadhem [aut],
Aristidis K. Nikoloulopoulos [aut, cre]
Estimation, model selection and goodness-of-fit of (1) factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2021) <doi:10.1111/bmsp.12231>; (2) bi-factor and second-order copula models for item response data in Kadhem and Nikoloulopoulos (2021) <arXiv:2102.10660>; (3) factor tree copula models for item response data in Kadhem and Nikoloulopoulos (2022) <arXiv:2201.00339>.

Identification of the Factors Affecting Species Richness
It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (<http://www.ipez.es/ModestR>) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
plotrix, relaimpo, nortest, lmtest, car, kernlab, usdm,
hier.part