factor-analysis

👑 Python factor analysis library (PCA, CA, MCA, MFA, FAMD)

an R package for structural equation modeling and more

GPU-accelerated Factors analysis library and Backtester

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.

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 Now almost entirely superseded by the models-by-example repo.

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) .

A Python module to perform exploratory factor analysis.

Multi-Omics Factor Analysis V2 (MOFA+)

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

Python machine learning applications in image processing and algorithm implementations including Matrix Completion, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

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

Descriptive probabilistic marker gene approach to single-cell pseudotime inference

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

Factor Analysis Kit

多因子指数增强策略/多因子全流程实现

PCA, Factor Analysis, CCA, Sparse Covariance Matrix Estimation, Imputation, Multiple Hypothesis Testing

Codebase for Cross-Spectral Factor Analysis (Gallagher et al., 2017)

Factor Modeling for radiomics

R package for factor analysis visualization

Case Study in ranking U.S. cities based on a single linear combination of rating variables. Dimensionality techniques used in the analysis are Principal Component Analysis (PCA), Factor Analysis (FA), Canonical Correlation Analysis (CCA)

From the given database Find out the personality using this personality traits. Applications in psychology Factor analysis has been used in the study of human intelligence and human personality as a method for comparing the outcomes of (hopefully) objective tests and to construct matrices to define correlations between these outcomes, as well as finding the factors for these results. The field of psychology that measures human intelligence using quantitative testing in this way is known as psychometrics (psycho=mental, metrics=measurement). Advantages 1)Offers a much more objective method of testing traits such as intelligence in humans 2)Allows for a satisfactory comparison between the results of intelligence tests 3)Provides support for theories that would be difficult to prove otherwise

Inference for Gaussian copula factor models and its application to causal discovery.

Probabilistic inference of single-cell bifurcations

Implementations of statistical models in python with an emphasis on multivariate models.

Alternating minimization approach to factor analysis

# kaefa kwangwoon automated exploratory factor analysis for improving research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment

We conduct simulation studies on dynamic factor analysis using maximum-likelihood and principal-component estimators.

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.