Transparent and Efficient Financial Analysis

👑 Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA

GPU-accelerated Factors analysis library and Backtester

an R package for structural equation modeling and more

An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.

Multi-Omics Factor Analysis

多因子指数增强策略/多因子全流程实现

A Python module to perform exploratory & confirmatory factor analyses.

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

psychometrics package, including MIRT(multidimension item response theory), IRT(item response theory),GRM(grade response theory),CAT(computerized adaptive testing), CDM(cognitive diagnostic model), FA(factor analysis), SEM(Structural Equation Modeling) .

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

A Java library for classical test theory, item response theory, factor analysis, and other measurement techniques. It provide tools commonly used in psychometrics and operational testing programs.

An R package for Bayesian structural equation modeling

Application and data for analyzing and structuring portfolios for climate investing.

Fast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-dimensional data

Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales

Object-oriented diagram plots with ggplot2

Scalable Ultra-Sparse Bayesian PCA

Descriptive probabilistic marker gene approach to single-cell pseudotime inference

Deep learning-based estimation and inference for item response theory models.