Skill UI

Guides post-training of GLM-class LLMs with Megatron-LM and SGLang, focusing on GRPO rollout generation, custom data buffers, and tight RL scaling workflows for research and production use.

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

Statsmodels is a robust Python library providing comprehensive tools for advanced statistical analysis. It supports a wide range of models, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM like Poisson and Logistic), Weighted Least Squares (WLS), and complex time series forecasting (ARIMA). Use this skill for rigorous econometrics, detailed diagnostic testing, and generating publication-ready statistical inference.