an R package for structural equation modeling and more
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Updated
Nov 27, 2025 - R
an R package for structural equation modeling and more
rstanarm R package for Bayesian applied regression modeling
An R package for Bayesian structural equation modeling
Basic statistical modelling examples.
Recursive Partitioning for Structural Equation Models
rstanarm R package for Bayesian applied regression modeling
An R package implementing Principal Component Pursuit for pattern recognition in environmental health.
Expand broom::tidy() output for categorical parameter estimates
An R package for Bayesian structural equation modeling using INLA
📊⚙️ Using 7 years of my sleep data, this project predicts Sleep Quality using a linear regression model based on predictors such as time in bed, time asleep, temperature, alarm, and steps.
MATH-342 Time Series course taken at EPFL during Spring 17-18.
An exploratory analysis of the Kaggle bikeshare data set with the application of linear regression models, which are not optimal for this particular problem of predicting bikes rented.
An R-based statistical analysis project using linear regression to estimate and visualize excess mortality across 31 Iranian provinces by modeling historical death rates.
Multi touch attribution models, including Markov chains
Professional R Data Science Portfolio: Advanced programming, ML pipelines, interactive Shiny dashboards, statistical modeling & production-ready code. Features 95%+ test coverage, Docker deployment, CI/CD workflows. Real-world applications in finance, healthcare & marketing. Perfect for showcasing R expertise to recruiters.
R package for statistical modeling with the Skellam distribution, supporting inference, random sampling, and regression for differences of independent Poisson counts.
Correspondence to Lancet regarding the article by Santos-Burgoa and colleagues (2018)
Apply empirical bias-reduced methods to fit a variety of latent variable models
Kaggle Titanic Data Set Using Logit Model
A series of Statistical Modelling assignments with the use of R. Applications of Linear, Polynomial, Logistic and Poisson Regression in various datasets
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