R

R

biostat (nem vettem fel, csak lementettem)

bioinfo

R package to create an interaction matrix from DPSIR variables, and to create a survey on KoboToolbox to ask for how variables affect each other.

An R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with antibiotic data by using evidence-based methods.

This script helps you find performance bottlenecks in downloaded, raw CI job logs

Helm chart for Shinyproxy

Fit parametric models for time-to-event data that show an initial 'incubation period', i.e., a variable phase where the hazard is zero. The delayed Weibull distribution serves as the foundational data model. The specific method of MPSE (maximum product of spacings estimation) or different variants of MLE (maximum likelihood estimation) are implemented for parameter estimation. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided as well.

Ansible collection for Data Science related tools

https://galaxy.ansible.com/ui/repo/published/devxy/data_science_core/

Containerfiles for R

Documents et site web à disposition des participants à la formation d'initiation à R. Website.

Cat Food diets investigation. This repository contains all the scripts and tables used for the research manuscript. UPDATES NEEDED when manuscript accepted/published

Domains.pinet.com

A mirror of https://git.sr.ht/~enricoschumann/PMwR

Mirrored to https://gitlab.b-data.ch/r/rsi and https://github.com/b-data/rsi.

Materials and resources for the teaching unit "Morphometrics with R" (University of Bordeaux, Summer School, 2024). Website.

Ansible collection for Posit applications

https://galaxy.ansible.com/ui/repo/published/devxy/posit/

This project investigates the impact of Forest Protected Area (FPA) designations on deforestation in the South American Atlantic Forest region, which spans Argentina, Brazil, and Paraguay. Leveraging remote-sensed forest cover data from 2000 to 2020 and geo-referenced socio-economic variables, the analysis applies a pseudo-randomized design to evaluate how FPA status influences forest loss. Results indicate that FPA designation reduced the odds of deforestation by approximately 14%, with protected areas exhibiting significantly lower deforestation rates (~5%) compared to non-protected forests (~19%). This effect size surpasses previous estimates from other Latin American regions, underscoring the critical role of conservation policy in this biodiversity hotspot. The project provides a reproducible framework for assessing FPA effectiveness and sets the stage for future research on the socio-economic impacts of forest conservation.

This project supports a spatial analysis of deforestation trends and drivers in the tri-national Paraná Atlantic Forest region, as presented in Mohebalian et al. (2022). Using geospatial data from Argentina, Brazil, and Paraguay, the study quantifies forest loss between 2000 and 2020 and evaluates the influence of factors such as road proximity, urban access, economic growth, and population density. The project uses statistical modeling and spatial data tools in R to estimate the effectiveness of Forest Protected Areas (FPAs) in mitigating deforestation. Results show heterogeneous policy impacts across countries, providing valuable insights for cross-border conservation planning and forest governance.

Probability and Statistics A regression model for predicting how patients will rate their pain in 12 months.