Agnostic Fay-Herriot Model for Small Area Statistics

Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors.

Exact Binary Sequential Designs and Analysis

For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.

Scans R Projects for Vulnerable Third Party Dependencies

Collects a list of your third party R packages, and scans them with the 'OSS' Index provided by 'Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use.

Random Survival Forest for Recurrent Events

Analyze recurrent events with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). 'recofest' extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (< https://hal.science/hal-04612431v1/document>).

Processing Linear Features

Assists in the manipulation and processing of linear features with the help of the 'sf' package. Makes use of linear referencing to extract data from most shape files. Reference for this packages methods: Albeke, S.E. et al. (2010) .

An API Client for the Environmental Data Initiative Repository

A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository < https://portal.edirepository.org/nis/home.jsp> is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack < https://pastaplus-core.readthedocs.io/en/latest/index.html#> and was developed in collaboration with the US 'LTER' Network < https://lternet.edu/>. 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users.

'Drat' R Archive Template

Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template.

Beyond the Border - Kernel Density Estimation for Urban Geography

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) , Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) .

Publication Toolkit for Water, Sanitation and Hygiene (WASH) Data

A toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management.

Multivariate Fay Herriot Models for Small Area Estimation

Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) .