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BGGM - Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

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openblascpp

8.45 score 2 stars 1 dependents 195 scripts 577 downloads

bmgarch - Bayesian Multivariate GARCH Models

Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.

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cpp

4.91 score 18 stars 8 scripts 359 downloads

GGMncv - Gaussian Graphical Models with Nonconvex Regularization

Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>, exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>, logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>, and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions for computing variable inclusion probabilities, multiple regression coefficients, and statistical inference <doi:10.1214/15-EJS1031>.

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openblascpp

4.40 score 1 stars 401 downloads

ivd - "Individual Variance Detection"

Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) <https://osf.io/sh6ne>.

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3.98 score 2 stars 19 scripts 133 downloads