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Stan development repository (home page is linked below). The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
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State space models (dynamic linear models, hidden Markov models) implemented in Stan.
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Implementation of B-Splines in Stan
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🚫 ↩️ A document that introduces Bayesian data analysis. -
Applied time series analysis in R with Stan. Allows fast Bayesian fitting of multivariate time-series models.
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Conditional autoregressive models in Stan
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idealstan offers item-response theory (IRT) ideal-point estimation for binary, ordinal, counts and continuous responses with time-varying and missing-data inference. Latent space model also included. Full and approximate Bayesian sampling with 'Stan' (www.mc-stan.org).
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Run standard t-tests, simulations, and Bayesian difference in means tests with R and Stan
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StanCon2018 Helsinki Tutorial
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Slides and assignments for an introductory course in hierarchical Bayesian modeling
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MTH225 Statistics for Science Spring 2016
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streamMetabolizer uses inverse modeling to estimate aquatic metabolism (photosynthesis and respiration) from time series data on dissolved oxygen, water temperature, depth, and light.
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Materials for BioC-2016 workshop entitled "Introduction to Bayesian Inference using Stan with Applications to Cancer Genomics"
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Various models of heterogeneous treatment effects
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Bayesian hierarchical models for estimating spatial and temporal patterns in vegetation phenology from Landsat time series
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Bayesian modelling of shot generation and conversion in soccer
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A workshop on using Stan with R.
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Multivariate Bayesian Structural Time Series in Stan
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Bayesian change point detection with R and Stan
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Learning rstan: R Interface to Stan
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Models for analyzing network data in which informant reports may be in conflict
