Maximum Likelihood Models for Species Abundance Distributions
Paulo I. Prado and Murilo Dantas Miranda
Maximum likelihood tools to fit and compare models of species
abundance distributions and species rank-abundance distributions.

Paulo I. Prado, Murilo Dantas Miranda and Andre Chalom
Maximum likelihood tools to fit and compare models of species
abundance distributions and of species rank-abundance distributions.

MASS, methods, graphics, grDevices, VGAM, poilog, GUILDS

Small Area Estimation using Fay-Herriot Models with Additive
Logistic Transformation
c(person("M. Rijalus", "Sholihin", role = c("aut", "cre"),
email = "221810400@stis.ac.id"),
person("Cucu", "Sumarni", role = c("aut"),
email = "cucu@stis.ac.id"))
Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP) estimator. The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is base on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.
stats, utils, magic, MASS, corpcor, progress, fpc
M. Rijalus Sholihin [aut, cre],
Cucu Sumarni [aut]

Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.