Transformations for Unit-Level Small Area Models
The aim of this package is to offer new methodology for unit-level
small area models under transformations and limited population auxiliary
information. In addition to this new methodology, the widely used nested
error regression model without transformations (see "An Error-Components
Model for Prediction of County Crop Areas Using Survey and Satellite Data"
by Battese, Harter and Fuller (1988) <doi:10.1080/01621459.1988.10478561>)
and its well-known uncertainty estimate (see "The estimation of the mean
squared error of small-area estimators" by Prasad and Rao (1990)
<doi:10.1080/01621459.1995.10476570>) are provided. In this package, the
log transformation and the data-driven log-shift transformation are
provided. If a transformation is selected, an appropriate method is chosen
depending on the respective input of the population data: Individual
population data (see "Empirical best prediction under a nested error model
with log transformation" by Molina and Martín (2018)
<doi:10.1214/17-aos1608>) but also aggregated population data (see
"Estimating regional income indicators under transformations and access to
limited population auxiliary information" by Würz, Schmid and Tzavidis
<unpublished>) can be entered. Especially under limited data access, new
methodologies are provided in 'saeTrafo'. Several options are available to
assess the used model and to judge, present and export its results. For a
detailed description of the package and the methods used see the
corresponding vignette.
nlme, emdi, stats, sfsmisc, parallelMap, MuMIn, ggplot2,
moments, openxlsx, reshape2, HLMdiag, gridExtra, stringr,
readODS
simFrame, sp, maptools, rgeos, graphics, R.rsp

Small Area Estimation using Empirical Bayes without Auxiliary
Variable
c(person(given = "Siti Rafika", family = "Fiandasari", role = c("aut","cre"), email = "fikafianda@gmail.com"),
person(given = "Margaretha Ari", family = "Anggorowati", role = "aut", email = "m.ari@bps.go.id"),
person(given = "Bahrul Ilmi", family = "Nasution", role = "aut", email = "bahrulnst@gmail.com"))
Estimates the parameter of small area in binary data without
auxiliary variable using Empirical Bayes technique, mainly from Rao
and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition".
This package provides another option of direct estimation using weight.
This package also features alpha and beta parameter estimation on calculating process of small area.
Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302>
and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.
Siti Rafika Fiandasari [aut, cre],
Margaretha Ari Anggorowati [aut],
Bahrul Ilmi Nasution [aut]