https://rast-lab.r-universe.devRecent package updates in rast-labR-universehttps://github.com/rast-lab.pnghttps://rast-lab.r-universe.devThu, 02 Jul 2026 23:52:20 GMTrast.ph@gmail.com (Philippe Rast)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>.https://github.com/r-universe/rast-lab/actions/runs/28629405315Thu, 02 Jul 2026 23:52:20 GMTBGGM2.1.6.9000successhttps://rast-lab.r-universe.devhttps://github.com/rast-lab/bggmcontrol.Rmdcontrol.htmlControlling for Variables2020-05-27 02:02:462024-06-21 23:11:08ppc_custom.Rmdppc_custom.htmlCustom Network Comparisons2020-05-19 21:54:442024-06-21 23:11:08netstat_custom.Rmdnetstat_custom.htmlCustom Network Statistics2020-05-20 16:11:392024-06-21 23:11:08var_model.Rmdvar_model.htmlGraphical VAR2020-06-04 17:01:362024-06-21 23:11:08in_tandem.Rmdin_tandem.htmlIn Tandem: Confirmatory and Exploratory Testing2020-05-24 04:54:502024-06-21 23:11:08mcmc_diagnostics.Rmdmcmc_diagnostics.htmlMCMC Diagnostics2020-05-21 02:52:322026-07-02 02:42:18netplot.Rmdnetplot.htmlNetwork Plots2020-05-21 15:10:502024-06-21 23:11:08predictability.Rmdpredictability.htmlPredictability: Binary, Ordinal, and Continuous2020-05-20 20:41:222024-06-21 23:11:08test_sum.Rmdtest_sum.htmlTesting Sums2020-05-25 21:56:342024-06-21 23:11:08hyp_3_ways.Rmdhyp_3_ways.htmlThree Ways to Test the Same Hypothesis2020-05-23 19:08:592024-06-21 23:11:08rast.ph@gmail.com (Philippe Rast)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>.https://github.com/r-universe/rast-lab/actions/runs/26936970002Wed, 29 Apr 2026 04:10:56 GMTGGMncv2.1.1.9000successhttps://rast-lab.r-universe.devhttps://github.com/rast-lab/ggmncvnct_custom.Rmdnct_custom.htmlCustom Network Comparison Tests2021-12-01 14:04:162021-12-01 14:04:16high_dim.Rmdhigh_dim.htmlHigh Dimensional Data: Must Read!!2021-12-01 14:04:162021-12-12 17:25:34cpu_time.Rmdcpu_time.htmlNCT: CPU Time2021-12-02 00:22:492021-12-02 00:22:49null_dist.Rmdnull_dist.htmlNCT: Null Distributions2021-12-01 16:43:292021-12-01 16:43:29sign_restrict.Rmdsign_restrict.htmlPositive Manifold (Sign Restriction)2021-11-28 17:05:202021-11-28 22:41:55