factor-analysis
There are 9 repositories under factor-analysis topic.
- FinanceToolkit
JerBouma / FinanceToolkit
Transparent and Efficient Financial Analysis
Language:Python 2802 - prince
MaxHalford / prince
:crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA
Language:Python 1249 - Language:Python 626
- Language:R 427
jerryxyx / AlphaTrading
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.
Language:Jupyter Notebook 324- MOFA2Language:R 295
phonegapX / alphasickle
多因子指数增强策略/多因子全流程实现
Language:Python 256EducationalTestingService / factor_analyzer
A Python module to perform exploratory & confirmatory factor analyses.
Language:Python 228- machine-learning
je-suis-tm / machine-learning
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, 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
naive-bayessupport-vector-machinestochastic-gradient-descentbatch-gradient-descentnewton-methodmultinomial-naive-bayeslinear-discriminant-analysissequential-minimal-optimizationk-nearest-neighborsdbscanexpectation-maximizationexpectation-maximization-algorithmfactor-analysisindependent-component-analysislatent-semantic-analysislow-rank-approximationsingular-value-decompositionridge-regressionlasso-regressionem-algorithmLanguage:Jupyter Notebook 220 inuyasha2012 / pypsy
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) .
Language:Python 215- Miscellaneous-R-Code
m-clark / Miscellaneous-R-Code
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**.
Language:R 193 meyerjp3 / psychometrics
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.
Language:Java 94- Language:R 85
gregversteeg / LinearCorex
Fast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-dimensional data
Language:Python 42opentaps / open-climate-investing
Application and data for analyzing and structuring portfolios for climate investing.
Language:JavaScript 42ashish-kamboj / Market-Mix-Modeling
Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales
Language:R 39kieranrcampbell / ouija
Descriptive probabilistic marker gene approach to single-cell pseudotime inference
Language:R 28mancusolab / susiepca
Scalable Ultra-Sparse Bayesian PCA
Language:Python 26Sagar-Darji / Personality-prediction
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
Language:Jupyter Notebook 21- Language:Python 19
JBris / multivariate_analysis_examples
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.
multivariate-analysismultivariate-regressionmultivariate-statisticsmultivariate-linear-regressionmultivariate-datamultivariatemultivariate-distributionsconcretepcapca-analysisfactor-analysiscanonical-correlation-analysisredundancy-analysispartial-least-squares-regressionpartial-least-squaresplsnmdsmdsdimension-reductiondimensionality-reductionLanguage:Python 18cjurban / deepirtools
Deep learning-based estimation and inference for item response theory models.
Language:Python 17- Language:Python 17
xinhe-lab / GSFA
R package that performs sparse factor analysis and differential gene expression discovery simultaneously on single-cell CRISPR screening data.
Language:C++ 17bghojogh / Generative-LLE
The code for Generative Locally Linear Embedding (GLLE).
Language:Python 15YasPHP / The-Five-Factor-Personality-Test
Unsupervised learning coupled with applied factor analysis to the five-factor model (FFM), a taxonomy for personality traits used to describe the human personality and psyche, via descriptors of common language and not on neuropsychological experiments. Used kmeans clustering and feature scaling (min-max normalization).
Language:Jupyter Notebook 15cuiruifei / CopulaFactorModel
Inference for Gaussian copula factor models and its application to causal discovery.
Language:R 14neil-gallagher / CSFA
Codebase for Cross-Spectral Factor Analysis (Gallagher et al., 2017)
Language:MATLAB 12- Language:R 11
ZechangSun / QFA
Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis
Language:Jupyter Notebook 11luca590 / High-Dimensional-Statistics
PCA, Factor Analysis, CCA, Sparse Covariance Matrix Estimation, Imputation, Multiple Hypothesis Testing
Language:R 10at-tan / Latent_Factors_in_Stocks
Dynamic factor modeling to uncover the key latent factors driving the price behavior of some of the largest American large-cap equities. We examine how these factors affect individual stock prices, what they represent, and how they have fluctuated in the sample period. As published in the Data Driven Investor on Medium.com.
Language:Jupyter Notebook 9TatevKaren / multivariate-statistics
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)
Language:R 8- Language:R 8
Marcosjnez / bifactor
Fit exploratory factor models and bi-factor models with multiple general factors.
Language:C++ 7p-gw / FactorRotations.jl
Rotation methods for factor analysis and principal component analysis in Julia
Language:Julia 7
