{ "_id": "6a102f06acfb0bcc41c94a00", "Type": "Package", "Package": "dsp", "Title": "Dynamic Shrinkage Process and Change Point Detection", "Version": "1.4.1", "Authors@R": "c(\nperson(\"Daniel R.\", \"Kowal\", , \"daniel.kowal@rice.edu\", role = c(\"aut\", \"cph\")),\nperson(\"Haoxuan\", \"Wu\", , \"hw399@cornell.edu\", role = c(\"aut\")),\nperson(\"Toryn\", \"Schafer\", , \"toryn27@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-5594-7697\")),\nperson(\"Jason B.\", \"Cho\", , \"bc454@cornell.edu\", role = \"aut\"),\nperson(\"David S.\", \"Matteson\", , \"matteson@cornell.edu\", role = \"aut\")\n)", "Description": "Provides efficient Markov chain Monte Carlo (MCMC)\nalgorithms for dynamic shrinkage processes, which extend\nglobal-local shrinkage priors to the time series setting by\nallowing shrinkage to depend on its own past. These priors\nyield locally adaptive estimates, useful for time series and\nregression functions with irregular features. The package\nincludes full MCMC implementations for trend filtering using\ndynamic shrinkage on signal differences, producing locally\nconstant or linear fits with adaptive credible bands. Also\nincluded are models with static shrinkage and\nnormal-inverse-Gamma priors for comparison. Additional tools\ncover dynamic regression with time-varying coefficients and\nB-spline models with shrinkage on basis differences, allowing\nfor flexible curve-fitting with unequally spaced data. Some\nsupport for heteroscedastic errors, outlier detection, and\nchange point estimation. Methods in this package are described\nin Kowal et al. (2019) , Wu et al.\n(2024) , Schafer and\nMatteson (2024) , and Cho\nand Matteson (2024) .", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "URL": "https://github.com/schafert/dsp", "BugReports": "https://github.com/schafert/dsp/issues", "Config/pak/sysreqs": "make", "Repository": "https://schafert.r-universe.dev", "Date/Publication": "2026-05-12 19:15:39 UTC", "RemoteUrl": "https://github.com/schafert/dsp", "RemoteRef": "HEAD", "RemoteSha": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-12 19:46:15 UTC", "User": "root" }, "Author": "Daniel R. Kowal [aut, cph],\nHaoxuan Wu [aut],\nToryn Schafer [aut, cre] (ORCID:\n),\nJason B. Cho [aut],\nDavid S. Matteson [aut]", "Maintainer": "Toryn Schafer ", "MD5sum": "02d08889a10c98428c5223d91f87809f", "_user": "schafert", "_type": "src", "_file": "dsp_1.4.1.tar.gz", "_fileid": "a6c8a324cf6700ea26f539552fa95ebdb991f4904957d6f63fe9d555936306b6", "_filesize": 365463, "_sha256": "a6c8a324cf6700ea26f539552fa95ebdb991f4904957d6f63fe9d555936306b6", "_created": "2026-05-12T19:46:15.000Z", "_published": "2026-05-22T10:25:10.232Z", "_distro": "noble", "_jobs": [ { "job": 77361175060, "time": 208, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "6954323780" }, { "job": 77361174973, "time": 253, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6954340600" }, { "job": 77361175073, "time": 228, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6954330058" }, { "job": 77361174972, "time": 214, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6954326051" }, { "job": 77361175190, "time": 205, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "6954322247" }, { "job": 77361174859, "time": 447, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "6954402232" }, { "job": 77361174758, "time": 171, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "6954310854" }, { "job": 77361174992, "time": 287, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6954349996" }, { "job": 77361174651, "time": 215, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6954252405" }, { "job": 77361174255, "time": 141, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7158286290" }, { "job": 77361174964, "time": 183, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "6954315100" }, { "job": 77361174921, "time": 233, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "6954331605" }, { "job": 77361174869, "time": 176, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "6954312579" } ], "_buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/schafert/dsp", "_commit": { "id": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "author": "Toryn Schafer <48369636+schafert@users.noreply.github.com>", "committer": "Toryn Schafer <48369636+schafert@users.noreply.github.com>", "message": "patch update for negbinomial typo\n", "time": 1778613339 }, "_maintainer": { "name": "Toryn Schafer", "email": "toryn27@gmail.com", "login": "schafert", "uuid": 48369636, "orcid": "0000-0001-5594-7697" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "RcppEigen", "role": "LinkingTo" }, { "package": "coda", "role": "Imports" }, { "package": "fda", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "MCMCpack", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "msm", "role": "Imports" }, { "package": "pgdraw", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "RcppZiggurat", "role": "Imports" }, { "package": "spam", "role": "Imports" }, { "package": "progress", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "stochvol", "role": "Imports" }, { "package": "BayesLogit", "role": "Imports" }, { "package": "truncdist", "role": "Imports" }, { "package": "mgcv", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "lifecycle", "role": "Imports" }, { "package": "glue", "role": "Imports" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "schafert", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-21", "n": 3 }, { "week": "2025-24", "n": 12 }, { "week": "2025-30", "n": 1 }, { "week": "2025-31", "n": 25 }, { "week": "2026-17", "n": 7 }, { "week": "2026-20", "n": 7 } ], "_tags": [], "_stars": 2, "_contributors": [ { "user": "schafert", "count": 124, "uuid": 48369636 }, { "user": "jasoncho0914", "count": 4, "uuid": 32781755 } ], "_userbio": { "uuid": 48369636, "type": "user", "name": "Toryn Schafer" }, "_downloads": { "count": 435, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/dsp" }, "_devurl": "https://github.com/schafert/dsp", "_searchresults": 6, "_topics": [ "cpp" ], "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/dsp.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/schafert/dsp", "_realowner": "schafert", "_cranurl": true, "_releases": [ { "version": "1.2.0", "date": "2025-08-19" }, { "version": "1.3.0", "date": "2026-04-20" } ], "_exports": [ "dsp_fit", "dsp_spec", "simRegression", "simUnivariate" ], "_help": [ { "page": "abco", "title": "Adaptive Bayesian Changepoint with Outliers", "topics": [ "abco" ] }, { "page": "btf", "title": "MCMC Sampler for Bayesian Trend Filtering", "topics": [ "btf" ] }, { "page": "btf_bspline", "title": "MCMC Sampler for B-spline Bayesian Trend Filtering", "topics": [ "btf_bspline" ] }, { "page": "btf_bspline0", "title": "MCMC Sampler for B-spline Bayesian Trend Filtering: D = 0", "topics": [ "btf_bspline0" ] }, { "page": "btf_reg", "title": "MCMC Sampler for Bayesian Trend Filtering: Regression", "topics": [ "btf_reg" ] }, { "page": "btf_sparse", "title": "Run the MCMC for sparse Bayesian trend filtering", "topics": [ "btf_sparse" ] }, { "page": "btf0", "title": "MCMC Sampler for Bayesian Trend Filtering: D = 0", "topics": [ "btf0" ] }, { "page": "build_Q", "title": "Compute the quadratic term in Bayesian trend filtering", "topics": [ "build_Q" ] }, { "page": "build_XtX", "title": "Compute X'X", "topics": [ "build_XtX" ] }, { "page": "computeDIC_ASV", "title": "Function for calculating DIC and Pb (Bayesian measures of model complexity and fit by Spiegelhalter et al. 2002)", "topics": [ "computeDIC_ASV" ] }, { "page": "credBands", "title": "Compute Simultaneous Credible Bands", "topics": [ "credBands" ] }, { "page": "dsp_fit", "title": "MCMC Sampler for Models with Dynamic Shrinkage Processes", "topics": [ "dsp_fit", "print.dsp" ] }, { "page": "dsp_spec", "title": "Model Specification", "topics": [ "dsp_spec", "print.dsp_spec" ] }, { "page": "ergMean", "title": "Compute the ergodic (running) mean.", "topics": [ "ergMean" ] }, { "page": "fit_ASV", "title": "MCMC Sampler for Adaptive Stchoastic Volatility (ASV) model", "topics": [ "fit_ASV" ] }, { "page": "fit_paramsASV", "title": "Helper function for Sampling parameters for ASV model", "topics": [ "fit_paramsASV" ] }, { "page": "fit_paramsASV_n", "title": "Helper function for Sampling parameters for ASV model with a nugget Effect", "topics": [ "fit_paramsASV_n" ] }, { "page": "generate_ly2hat", "title": "Posterior predictive sampler on the transformed y (log(y^2))", "topics": [ "generate_ly2hat" ] }, { "page": "getARpXmat", "title": "Compute the design matrix X for AR(p) model", "topics": [ "getARpXmat" ] }, { "page": "getEffSize", "title": "Summarize of effective sample size", "topics": [ "getEffSize" ] }, { "page": "getNonZeros", "title": "Compute Non-Zeros (Signals)", "topics": [ "getNonZeros" ] }, { "page": "init_paramsASV", "title": "Helper function for initializing parameters for ASV model", "topics": [ "init_paramsASV" ] }, { "page": "init_paramsASV_n", "title": "Helper function for initializing parameters for ASV model with a nugget effect", "topics": [ "init_paramsASV_n" ] }, { "page": "initChol_spam", "title": "Compute initial Cholesky decomposition for Bayesian Trend Filtering", "topics": [ "initChol_spam" ] }, { "page": "initCholReg_spam", "title": "Compute initial Cholesky decomposition for TVP Regression", "topics": [ "initCholReg_spam" ] }, { "page": "initDHS", "title": "Initialize the evolution error variance parameters", "topics": [ "initDHS" ] }, { "page": "initEvol0", "title": "Initialize the parameters for the initial state variance", "topics": [ "initEvol0" ] }, { "page": "initEvolParams", "title": "Initialize the evolution error variance parameters", "topics": [ "initEvolParams" ] }, { "page": "initSV", "title": "Initialize the stochastic volatility parameters", "topics": [ "initSV" ] }, { "page": "invlogit", "title": "Compute the inverse log-odds", "topics": [ "invlogit" ] }, { "page": "logit", "title": "Compute the log-odds", "topics": [ "logit" ] }, { "page": "ncind", "title": "Sample components from a discrete mixture of normals", "topics": [ "ncind" ] }, { "page": "plot.dsp", "title": "Plot the Bayesian trend filtering fitted values", "topics": [ "plot.dsp" ] }, { "page": "predict.dsp", "title": "Predict changepoints from the output of ABCO", "topics": [ "predict.dsp" ] }, { "page": "sample_j_wrap", "title": "Sampling from 10-component Gaussian Mixture component described in Omori et al. 2007", "topics": [ "sample_j_wrap" ] }, { "page": "sample_mat_c", "title": "Wrapper function for C++ call for sample mat, check pre-conditions to prevent crash", "topics": [ "sample_mat_c" ] }, { "page": "sampleAR1", "title": "Sample the AR(1) coefficient(s)", "topics": [ "sampleAR1" ] }, { "page": "sampleBTF", "title": "Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)", "topics": [ "sampleBTF" ] }, { "page": "sampleBTF_bspline", "title": "Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)", "topics": [ "sampleBTF_bspline" ] }, { "page": "sampleBTF_reg", "title": "Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)", "topics": [ "sampleBTF_reg" ] }, { "page": "sampleBTF_reg_backfit", "title": "(Backfitting) Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)", "topics": [ "sampleBTF_reg_backfit" ] }, { "page": "sampleBTF_sparse", "title": "Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM) with additional shrinkage to zero", "topics": [ "sampleBTF_sparse" ] }, { "page": "sampleDSP", "title": "Sample the dynamic shrinkage process parameters", "topics": [ "sampleDSP" ] }, { "page": "sampleEvol0", "title": "Sample the parameters for the initial state variance", "topics": [ "sampleEvol0" ] }, { "page": "sampleEvolParams", "title": "Sampler evolution error variance parameters", "topics": [ "sampleEvolParams" ] }, { "page": "sampleFastGaussian", "title": "Sample a Gaussian vector using the fast sampler of BHATTACHARYA et al.", "topics": [ "sampleFastGaussian" ] }, { "page": "sampleLogVolMu", "title": "Sample the AR(1) unconditional means", "topics": [ "sampleLogVolMu" ] }, { "page": "sampleLogVolMu0", "title": "Sample the mean of AR(1) unconditional means", "topics": [ "sampleLogVolMu0" ] }, { "page": "sampleLogVols", "title": "Sample the latent log-volatilities", "topics": [ "sampleLogVols" ] }, { "page": "sampleSVparams", "title": "Sampler for the stochastic volatility parameters", "topics": [ "sampleSVparams" ] }, { "page": "sampleSVparams0", "title": "Sampler for the stochastic volatility parameters using same functions as DHS prior", "topics": [ "sampleSVparams0" ] }, { "page": "simBaS", "title": "Compute Simultaneous Band Scores (SimBaS)", "topics": [ "simBaS" ] }, { "page": "simRegression", "title": "Simulate noisy observations from a dynamic regression model", "topics": [ "simRegression" ] }, { "page": "simRegression0", "title": "Simulate noisy observations from a dynamic regression model", "topics": [ "simRegression0" ] }, { "page": "simUnivariate", "title": "Generate univariate signals of different type", "topics": [ "simUnivariate" ] }, { "page": "spec_dsp", "title": "Compute the spectrum of an AR(p) model", "topics": [ "spec_dsp" ] }, { "page": "summary.dsp", "title": "Summarize DSP MCMC chains", "topics": [ "summary.dsp" ] }, { "page": "t_create_loc", "title": "Initializer for location indices for filling in band-sparse matrix", "topics": [ "t_create_loc" ] }, { "page": "t_initEvolParams_no", "title": "Initialize the evolution error variance parameters", "topics": [ "t_initEvolParams_no" ] }, { "page": "t_initEvolZeta_ps", "title": "Initialize the anomaly component parameters", "topics": [ "t_initEvolZeta_ps" ] }, { "page": "t_initSV", "title": "Initialize the stochastic volatility parameters", "topics": [ "t_initSV" ] }, { "page": "t_sampleAR1", "title": "Sample the TAR(1) coefficients", "topics": [ "t_sampleAR1" ] }, { "page": "t_sampleBTF", "title": "Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)", "topics": [ "t_sampleBTF" ] }, { "page": "t_sampleEvolParams", "title": "Sample the thresholded dynamic shrinkage process parameters", "topics": [ "t_sampleEvolParams" ] }, { "page": "t_sampleEvolZeta_ps", "title": "Sampler for the anomaly component parameters", "topics": [ "t_sampleEvolZeta_ps" ] }, { "page": "t_sampleLogVolMu", "title": "Sample the TAR(1) unconditional means", "topics": [ "t_sampleLogVolMu" ] }, { "page": "t_sampleLogVols", "title": "Sample the latent log-volatilities", "topics": [ "t_sampleLogVols" ] }, { "page": "t_sampleR_mh", "title": "Sample the threshold parameter", "topics": [ "t_sampleR_mh" ] }, { "page": "t_sampleSVparams", "title": "Sampler for the stochastic volatility parameters", "topics": [ "t_sampleSVparams" ] }, { "page": "uni.slice", "title": "Univariate Slice Sampler from Neal (2008)", "topics": [ "uni.slice" ] } ], "_readme": "https://github.com/schafert/dsp/raw/HEAD/README.md", "_rundeps": [ "ash", "BayesLogit", "bitops", "cli", "cluster", "coda", "colorspace", "cpp11", "crayon", "deSolve", "dotCall64", "evd", "expm", "farver", "fda", "fds", "FNN", "generics", "ggplot2", "glue", "gtable", "hdrcde", "hms", "isoband", "kernlab", "KernSmooth", "ks", "labeling", "lattice", "lifecycle", "locfit", "magrittr", "MASS", "Matrix", "MatrixModels", "mclust", "mcmc", "MCMCpack", "mgcv", "msm", "multicool", "mvtnorm", "nlme", "pcaPP", "pgdraw", "pillar", "pkgconfig", "pracma", "prettyunits", "progress", "purrr", "quantreg", "R6", "rainbow", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "RcppGSL", "RcppZiggurat", "RCurl", "rlang", "S7", "scales", "spam", "SparseM", "stochvol", "survival", "tibble", "truncdist", "utf8", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_score": 3.778151250383644, "_indexed": true, "_nocasepkg": "dsp", "_universes": [ "schafert" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.4.1", "date": "2026-05-12T19:49:11.000Z", "distro": "noble", "arch": "aarch64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "75cd7925748a3e94313ef35579e292e3103d90c5f58f4135918bf06bd869fc0a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.7.0", "os": "linux", "version": "1.4.1", "date": "2026-05-12T19:49:48.000Z", "distro": "noble", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "5829449435c7c10df05a48b3f55e0bca63eae3e1f7116aafcb58d9225c128576", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.6.0", "os": "linux", "version": "1.4.1", "date": "2026-05-12T19:49:24.000Z", "distro": "noble", "arch": "aarch64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "f8f81333d4e3a0ad0f72b14bdac0295bc67ed0924b7fcd4773d6135488b91a84", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.6.0", "os": "linux", "version": "1.4.1", "date": "2026-05-12T19:49:09.000Z", "distro": "noble", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "96bc464d77502d1ce87c82b9a414aadc41a010c90311ef97edfe103bcb4af7c0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.5.3", "os": "mac", "version": "1.4.1", "date": "2026-05-12T19:48:46.000Z", "arch": "aarch64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "3affc4c9f3725cce4008b8242be454c0130f3f8c35f0f7b7ebe6281c9158c01c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.5.3", "os": "mac", "version": "1.4.1", "date": "2026-05-12T19:51:10.000Z", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "8942f7e2dcbabb265e4a2c7618802660c8fb497c2a3add76ac6287ddf30505aa", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.6.0", "os": "mac", "version": "1.4.1", "date": "2026-05-12T19:48:25.000Z", "arch": "aarch64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "bf146ea9bebeb62501234d552f17c74eb53871db1c1e7feee10502da5f532a39", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.6.0", "os": "mac", "version": "1.4.1", "date": "2026-05-12T19:49:47.000Z", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "7ae68f4fb7eb880df9e406222a21942557d9fdc13d2fdf69946da31e58328ca5", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.7.0", "os": "win", "version": "1.4.1", "date": "2026-05-12T19:48:02.000Z", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "9d55398a7bed4f50a02ab12c664c21ba5eef29034d7d23fac3158e9174fa0f70", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.5.3", "os": "win", "version": "1.4.1", "date": "2026-05-12T19:48:40.000Z", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "0117fea89c5e587aebdd87f8df9c8e86dfc85714fef94671c2fc73b434eb788d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.6.0", "os": "win", "version": "1.4.1", "date": "2026-05-12T19:47:53.000Z", "arch": "x86_64", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "15bc1145798546cd2378056f1c702610edc99444fe9ea3a7255b33bea8b44cf8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" }, { "r": "4.6.0", "os": "wasm", "version": "1.4.1", "date": "2026-05-22T10:24:31.000Z", "arch": "emscripten", "commit": "893858ce2cc73d18486bd4f4a0d4493d2dd304d2", "fileid": "a94a60ea65541ab0623ceaef01c4840525ad4f0806c454711589ec40f22a77da", "status": "success", "buildurl": "https://github.com/r-universe/schafert/actions/runs/25758010733" } ] }