{ "_id": "6ac9ee64fe5cc25786e82ec7", "Package": "neuralsbi", "Title": "Neural Simulation-Based Inference", "Version": "0.6.83", "Authors@R": "person(\"Pedro\", \"Nascimento de Lima\", email = \"plima@rand.org\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-9057-198X\"))", "Description": "A native R implementation of neural simulation-based\ninference (SBI) methods. Given a prior over parameters and a\nsimulator, 'neuralsbi' trains a conditional neural density\nestimator for likelihood-free Bayesian inference: Neural\nPosterior Estimation approximates the posterior directly and\nsamples it in a forward pass, Neural Likelihood Estimation\nlearns a surrogate likelihood that is sampled with MCMC,\nhandles repeated independent observations, and can be exported\nas 'Stan' code for use inside a larger model, and Neural Ratio\nEstimation trains a classifier for the likelihood-to-evidence\nratio instead of either density. Neural estimators run on the\n'torch' back end. This package is developed for applied\nresearchers who want an R-native approachable SBI interface\nwith sensible defaults and built-in posterior diagnostics.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "Additional_repositories": "https://mc-stan.org/r-packages/", "Config/testthat/edition": "3", "Config/Needs/coverage": "covr", "VignetteBuilder": "knitr", "URL": "https://neuralsbi.pedrodelima.com/,\nhttps://github.com/pedroliman/neuralsbi", "BugReports": "https://github.com/pedroliman/neuralsbi/issues", "Config/roxygen2/version": "8.1.0", "Repository": "https://pedroliman.r-universe.dev", "Date/Publication": "2026-10-10 05:58:52 UTC", "RemoteUrl": "https://github.com/pedroliman/neuralsbi", "RemoteRef": "HEAD", "RemoteSha": "48817e3e98c089337dccea58f6612fcf5e64d10b", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-10 07:35:41 UTC", "User": "root" }, "Author": "Pedro Nascimento de Lima [aut, cre] (ORCID:\n)", "Maintainer": "Pedro Nascimento de Lima ", "_user": "pedroliman", "_type": "src", "_file": "neuralsbi_0.6.83.tar.gz", "_fileid": "https://r2.ropensci.org/fdf63aa7673b1b091d1d066bc0305a1e086d380b12d2c4fa660f2ff91f2eb41e", "_filesize": 1479520, "_sha256": "fdf63aa7673b1b091d1d066bc0305a1e086d380b12d2c4fa660f2ff91f2eb41e", "_expires": "2027-01-18T07:50:58.000Z", "_created": "2026-10-10T07:35:41.000Z", "_published": "2026-10-10T07:51:00.084Z", "_buildurl": "https://github.com/r-universe/pedroliman/actions/runs/38034750740", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 114163447712, "time": 407, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11664310339" }, { "job": 114163447687, "time": 283, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11663990852" }, { "job": 114163447731, "time": 303, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11663374205" }, { "job": 114163447732, "time": 239, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11664096636" }, { "job": 114162797713, "time": 227, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11662364947" }, { "job": 114163447700, "time": 142, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11664385432" }, { "job": 114163447738, "time": 392, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "11664190927" }, { "job": 114163447716, "time": 442, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "ERROR", "artifact": "11664375860" }, { "job": 114163447759, "time": 456, "config": "windows-release-x86_64", "r": "4.6.1", "check": "ERROR", "artifact": "11664280883" } ], "_upstream": "https://github.com/pedroliman/neuralsbi", "_commit": { "id": "48817e3e98c089337dccea58f6612fcf5e64d10b", "author": "Pedro Nascimento de Lima ", "committer": "GitHub ", "message": "Fix prior_truncated() for upper-tail truncation (#386)\n\n* Sample and normalize prior_truncated() on the tail that holds the bounds\n\nThe truncated mass and the inverse-CDF uniform came from the lower-tail CDF only, so a lower bound past the median rounded the CDF to 1 and produced quantised or Inf draws and a wrong normaliser. new_marginal() now works on the log scale of the smaller tail and marginal_sample() draws on it. 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