dose-response

Analysis platform for large-scale dose-dependent data

U.S EPA Benchmark Dose Modeling Software (BMDS)

Dose-response data analysis

Generate Dose-Response Curves in Python

Refactored version of the drc package, a framework for fitting and analyzing dose–response models. This repository restructures the codebase to improve maintainability, transparency, and future development.

U.S. EPA Benchmark Dose Modeling Software User Interface (BMDS Desktop and BMDS Online)

Stata package for difference-in-differences with a continuous treatment (Callaway–Goodman-Bacon–Sant'Anna 2024): B-spline dose-response ATT(d) & ACRT(d), uniform confidence bands, staggered adoption.

DReamGAMM: Dose-Response EnhAnced Modeling by Generalized Additive Mixed Model

Accounting for hidden confounders in estimates of dose-response curves from observational data.

Dose-Response Meta-Regression for Meta-Analysis

Streamlit version of py50

Nextcast: a software suite to analyse and model toxicogenomics data

Open-source Python toolkit for reproducible enzyme kinetics, Michaelis-Menten fitting, and IC50 dose-response analysis.

Protocol for plotting calibration curves and beam profiles from film with MATLAB & ImageJ

Reproducible regulatory transfer analysis for CRISPR interference in primary human CD4 T cells

R -based meta-analysis software with pairwise meta-analysis, dose-response meta-analysis, meta-regression, and publication bias diagnostics — packaged as a standalone Windows desktop app (Electron + embedded R).

software for 4PL curve fitting

Native R package implementing T-HAL-MLE (Targeted Highly Adaptive Lasso Minimum Loss Estimation) for continuous dose-response curves. 100% R base, zero external dependencies.

Open-source desktop application for OECD TG 236 zebrafish embryo toxicity (FET) assays — experimental design, daily well scoring, LC50/NOEC/LOEC and sublethal endpoint analysis, OECD validity checks, and automated Word report generation. Developed by Henrique Tamanini S. Moschen, University of Brasilia.

More screening decisions from fewer measurements: from three concentrations, AnchorBoost reports 925 of 965 held-out designs, 69 wrong, on 10.2% fewer exposure wells; its stack beats all 32 published predictor configurations.