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stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 10 days ago.

bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp

393 stars 15.70 score 5.0k scripts 13 dependents

greta-dev

greta:Simple and Scalable Statistical Modelling in R

Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.

Maintained by Nicholas Tierney. Last updated 19 days ago.

566 stars 12.53 score 396 scripts 6 dependents

pbs-assess

sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.

Maintained by Sean C. Anderson. Last updated 17 hours ago.

ecologyglmmspatial-analysisspecies-distribution-modellingtmbcpp

205 stars 11.04 score 848 scripts 1 dependents

dmphillippo

multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.

Maintained by David M. Phillippo. Last updated 3 days ago.

statisticscpp

35 stars 9.34 score 163 scripts

azure

azuremlsdk:Interface to the 'Azure Machine Learning' 'SDK'

Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.

Maintained by Diondra Peck. Last updated 3 years ago.

amlcomputeazureazure-machine-learningazuremldsimachine-learningrstudiosdk-r

105 stars 8.91 score 221 scripts

connordonegan

geostan:Bayesian Spatial Analysis

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

Maintained by Connor Donegan. Last updated 3 months ago.

bayesianbayesian-inferencebayesian-statisticsepidemiologymodelingpublic-healthrspatialspatialstancpp

80 stars 8.80 score 46 scripts

biodiverse

ubms:Bayesian Models for Data from Unmarked Animals using 'Stan'

Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Maintained by Ken Kellner. Last updated 1 months ago.

distance-samplinghierarchical-modelsn-mixture-modeloccupancystanopenblascpp

36 stars 7.90 score 73 scripts

leoegidi

footBayes:Fitting Bayesian and MLE Football Models

This is the first package allowing for the estimation, visualization and prediction of the most well-known football models: double Poisson, bivariate Poisson, Skellam, student_t, diagonal-inflated bivariate Poisson, and zero-inflated Skellam. It supports both maximum likelihood estimation (MLE, for 'static' models only) and Bayesian inference. For Bayesian methods, it incorporates several techniques: MCMC sampling with Hamiltonian Monte Carlo, variational inference using either the Pathfinder algorithm or Automatic Differentiation Variational Inference (ADVI), and the Laplace approximation. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package. The model construction relies on the most well-known football references, such as Dixon and Coles (1997) <doi:10.1111/1467-9876.00065>, Karlis and Ntzoufras (2003) <doi:10.1111/1467-9884.00366> and Egidi, Pauli and Torelli (2018) <doi:10.1177/1471082X18798414>.

Maintained by Leonardo Egidi. Last updated 13 days ago.

42 stars 7.43 score 53 scripts

wwiecek

baggr:Bayesian Aggregate Treatment Effects

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

Maintained by Witold Wiecek. Last updated 5 days ago.

bayesian-statisticsmeta-analysisquantile-regressionstantreatment-effectscpp

49 stars 7.24 score 88 scripts

trinker

wakefield:Generate Random Data Sets

Generates random data sets including: data.frames, lists, and vectors.

Maintained by Tyler Rinker. Last updated 5 years ago.

data-generationwakefield

256 stars 7.13 score 209 scripts

flyaflya

causact:Fast, Easy, and Visual Bayesian Inference

Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.

Maintained by Adam Fleischhacker. Last updated 2 months ago.

bayesian-inferencedagsposterior-probabilityprobabilistic-graphical-modelsprobabilistic-programming

45 stars 6.97 score 52 scripts

asael697

bayesforecast:Bayesian Time Series Modeling with Stan

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.

bayesian-inferenceforecasting-modelsmcmcstantime-series-analysiscpp

45 stars 6.92 score 62 scripts

helske

bssm:Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Maintained by Jouni Helske. Last updated 7 months ago.

bayesian-inferencecppmarkov-chain-monte-carloparticle-filterstate-spacetime-seriesopenblascppopenmp

42 stars 6.43 score 11 scripts

jtimonen

lgpr:Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Maintained by Juho Timonen. Last updated 7 months ago.

bayesian-inferencegaussian-processeslongitudinal-datastancpp

25 stars 5.94 score 69 scripts

connordonegan

surveil:Time Series Models for Disease Surveillance

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).

Maintained by Connor Donegan. Last updated 9 months ago.

bayesian-statisticscancerhealth-equitypublic-healthrstancpp

2 stars 4.98 score 12 scripts

andriyprotsak5

UAHDataScienceSF:Interactive Statistical Learning Functions

An educational toolkit for learning statistical concepts through interactive exploration. Provides functions for basic statistics (mean, variance, etc.) and probability distributions with step-by-step explanations and interactive learning modes. Each function can be used for simple calculations, detailed learning with explanations, or interactive practice with feedback.

Maintained by Andriy Protsak Protsak. Last updated 1 months ago.

3.30 score

ehsan66

ICAOD:Optimal Designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm (ICA)

Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Maintained by Ehsan Masoudi. Last updated 4 years ago.

cpp

2.49 score 31 scripts

pettermostad

lestat:A Package for Learning Statistics

Some simple objects and functions to do statistics using linear models and a Bayesian framework.

Maintained by Petter Mostad. Last updated 7 years ago.

2.28 score 64 scripts 1 dependents

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