Mixed Effect Random Forests for Small Area Estimation
person("Patrick", "Krennmair", email = "patrick.krennmair@fu-berlin.de", role = c("aut", "cre"))
Mixed Effects Random Forests (MERFs) are a data-driven,
nonparametric alternative to current methods of Small Area Estimation
(SAE). 'SAEforest' provides functions for the estimation of regionally
disaggregated linear and nonlinear indicators using survey sample
data. Included procedures facilitate the estimation of domain-level
economic and inequality metrics and assess associated uncertainty.
Emphasis lies on straightforward interpretation and visualization of results.
From a methodological perspective, the package builds on approaches discussed in
Krennmair and Schmid (2022) <arXiv:2201.10933v2> and Krennmair
et al. (2022) <arXiv:2204.10736>.
caret, dplyr, ggplot2, haven, ineq, lme4, maptools, pbapply,
pdp, ranger, reshape2, stats, vip

Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.
c(person(given = "Andrea",
family = "Fasulo",
role = c("aut", "cre"),
email = "fasulo@istat.it"))

Shape Analysis for AI-Reconstructed Images
c(
person(given = "Esteban",
family = "Fernandez Morales",
role = c("aut", "cre"),
email = "esteban.fernandezmorales@utdallas.edu"),
person(given = "Qiwei",
family = "Li",
role = c("aut"),
email = "qiwei.li@utdallas.edu"))
Provides functionality for image processing and shape analysis in
the context of reconstructed medical images generated by deep learning-based
methods or standard image processing algorithms and produced from different
medical imaging types, such as X-ray, Computational Tomography (CT),
Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically,
offers tools to segment regions of interest and to extract quantitative
shape descriptors for applications in signal processing,
statistical analysis and modeling, and machine learning.
caTools, EBImage, graphics, lattice, png
Esteban Fernandez Morales [aut, cre],
Qiwei Li [aut]