https://lizhongc.r-universe.devRecent package updates in lizhongcR-universehttps://github.com/lizhongc.pnghttps://lizhongc.r-universe.devTue, 07 Jul 2026 00:52:55 GMTchen.l@wehi.edu.au (Lizhong Chen)Computes various Local Indicators of Spatial Association (LISA) statistics, including univariate and bivariate local Moran's I, Empirical Bayes local Moran's I, univariate and multivariate local Geary's C, and Getis-Ord G and G* statistics. The methods follow Anselin (1995), Getis and Ord (1992), and Anselin (2019). Leverages a high-performance, plain-C backend with optional 'OpenMP' multi-core support for fast permutation-based pseudo-p-value calculation. Accepts any 'spdep' listw spatial weight matrix, including custom and non-contiguity weights. Uses sample standardisation (n-1) and 'rgeoda'-style permutation p-values. Output cluster codes match 'rgeoda' conventions, including the Isolated category for observations without neighbours.https://github.com/r-universe/lizhongc/actions/runs/28852455110Tue, 07 Jul 2026 00:52:55 GMTfastLISA1.0.1successhttps://lizhongc.r-universe.devhttps://github.com/lizhongc/fastlisafastLISA.RnwfastLISA.pdfComputing LISA statistics with fastLISA2026-06-20 14:32:022026-07-07 00:49:51chen.l@wehi.edu.au (Lizhong Chen)Variable selection for generalized linear models and the Cox proportional-hazards model in ultrahigh dimensions via the iterated block Gibbs sampler (IBGS). The sampler is implemented in C with parallel block screening through 'OpenMP', and supports the gaussian, binomial and poisson families (fitted by least squares or iteratively reweighted least squares) as well as the Cox model for survival analysis (fitted by its Efron partial likelihood), together with the AIC, BIC, AICc and extended BIC model selection criteria.https://github.com/r-universe/lizhongc/actions/runs/28734469596Mon, 29 Jun 2026 21:46:49 GMTIBGS1.0.0successhttps://lizhongc.r-universe.devhttps://github.com/lizhongc/ibgsIBGS.RnwIBGS.pdfGetting started with IBGS2026-06-21 10:20:052026-06-29 07:10:44