bin2norm - Hierarchical Probit Estimation for Dichotomized Data

Provides likelihood-based and hierarchical estimation methods for thresholded (binomial-probit) data. Supports fixed-mean and random-mean models with maximum likelihood estimation (MLE), generalized linear mixed model (GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations. For methodological background, see Albert and Chib (1993) <doi:10.1080/01621459.1993.10476321> and McCulloch (1994) <doi:10.2307/2297959>.

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2.00 score 467 downloads

iimi - Identifying Infection with Machine Intelligence

A novel machine learning method for plant viruses diagnostic using genome sequencing data. This package includes three different machine learning models, random forest, XGBoost, and elastic net, to train and predict mapped genome samples. Mappability profile and unreliable regions are introduced to the algorithm, and users can build a mappability profile from scratch with functions included in the package. Plotting mapped sample coverage information is provided.

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2.00 score 10 scripts 190 downloads

scAnnotate - An Automated Cell Type Annotation Tool for Single-Cell RNA-Sequencing Data

An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.

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2.00 score 4 scripts 253 downloads

ebdm - Estimating Bivariate Dependency from Marginal Data

Provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. The package supports three modules of bivariate joint distribution estimated from marginal summary data: (1) two binary, (2) two continuous, (3) one binary and one continuous These methods enable privacy-preserving joint estimation when individual-level data are unavailable. The approaches are detailed in Shang, Tsao, and Zhang (2025a) <doi:10.48550/arXiv.2505.03995> and Shang, Tsao, and Zhang (2025b) <doi:10.48550/arXiv.2508.02057>.

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1.60 score 2 scripts 507 downloads

glmpermu - Permutation-Based Inference for Generalized Linear Models

In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.

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1.00 score 195 downloads

bossR - Biomarker Optimal Segmentation System

The Biomarker Optimal Segmentation System R package, 'bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that 'bossR' offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.

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1.00 score 187 downloads