Ranger helps you see the forest among the trees - Ranger is an effect-size meta analysis library creating beautiful forest plots!
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Updated
Jun 12, 2023 - Python
Ranger helps you see the forest among the trees - Ranger is an effect-size meta analysis library creating beautiful forest plots!
This repository highlights the significant associations between maternal nutrition interventions (such as iron and folic acid supplementation, maternal hemoglobin levels,& dietary diversity score) and the likelihood of low birth weight (LBW) in developing nations. The analysis reveals varying effect sizes and degrees of heterogeneity among studies.
Semi-automatic estimation of the tree biomass using terrestrial LiDAR data.
This repository examines the relationship between maternal nutrition interventions, including iron-folic acid supplementation, maternal hemoglobin levels, and dietary diversity, and the risk of low birth weight (LBW) in developing countries, highlighting varied effect sizes and study heterogeneity.
R package to create publication-ready forest plots.
Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation of RecSys Experiments
Meta-analysis on discriminant validity (Hedges g) of various cognitive tests. Risk of bias and forest plots
Applied a Cox Proportional Hazards model in Python to identify prognostic factors associated with survival in pediatric candidemia patients. Estimated hazard ratios, evaluated clinical covariates, and interpreted survival outcomes.
Code repository for the manuscript: 'An umbrella review of health outcomes following traumatic brain injury' (published in Nature Mental Health)
Generate a forest plot graph to display LOEUF scores.
This repository provides a fully reproducible R script for random‑effects network meta‑analysis of continuous outcomes, including data preparation, inconsistency assessment, treatment ranking, and publication bias evaluation.
Reproducible public health statistics workflow with descriptive epidemiology, age-adjusted regression, forest plots, and Quarto reporting.
Generate two forest plot graphs to display HBOC Genes burden test.
Journal-ready forest plots with uniform spacing, embedded fonts, and automated sizing for Mendelian-randomization (or any β/CI) results.
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