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soerenpannier

emdi:Estimating and Mapping Disaggregated Indicators

Functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".

Maintained by Soeren Pannier. Last updated 1 years ago.

15 stars 7.26 score 45 scripts 1 dependents

bioc

DAPAR:Tools for the Differential Analysis of Proteins Abundance with R

The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package).

Maintained by Samuel Wieczorek. Last updated 5 months ago.

proteomicsnormalizationpreprocessingmassspectrometryqualitycontrolgodataimportprostar1

2 stars 5.42 score 22 scripts 1 dependents

ssa-statistical-team-projects

povmap:Extension to the 'emdi' Package

The R package 'povmap' supports small area estimation of means and poverty headcount rates. It adds several new features to the 'emdi' package (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). These include new options for incorporating survey weights, ex-post benchmarking of estimates, two additional transformations, several new convenient functions to assist with reporting results, and a wrapper function to facilitate access from 'Stata'.

Maintained by Ifeanyi Edochie. Last updated 5 months ago.

1 stars 4.60 score 10 scripts

norawuerz

saeTrafo: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.

Maintained by Nora Würz. Last updated 11 months ago.

1 stars 3.70 score

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