Title: Mixed Effect Random Forests for Small Area Estimation
Authors@R: person("Patrick", "Krennmair", email = "patrick.krennmair@fu-berlin.de", role = c("aut", "cre"))
Description: 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
Imports: caret, dplyr, ggplot2, haven, ineq, lme4, maptools, pbapply,
Suggests: R.rsp, sp, rgeos, testthat (>= 3.0.0)

Description: Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.

Title: Shape Analysis for AI-Reconstructed Images
Description: 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.
Imports: caTools, EBImage, graphics, lattice, png
Packaged: 2021-02-23 19:34:09 UTC; estfernandez
Author: Esteban Fernandez Morales [aut, cre],
Maintainer: Esteban Fernandez Morales <esteban.fernandezmorales@utdallas.edu>

Title: Statistical Analysis of Fuzzy Data
for doing statistics with one dimensional Fuzzy Data (in the
form of polygonal fuzzy numbers). In particular, the package
contains functions for the basic operations on the class of
fuzzy numbers (sum, scalar product, mean, Hukuhara difference)
as well as for calculating (Bertoluzza) distance, sample
variance, sample covariance, sample correlation, and the
Dempster-Shafer (levelwise) histogram. Moreover a function to
simulate fuzzy random variables, bootstrap tests for the
equality of means, and a function to do linear regression given
Packaged: 2009-10-23 07:23:14 UTC; trutschnig