{ "_id": "6a1ed94eb401979e7340f305", "Package": "GLDEX", "Version": "2.0.0.9.4", "Date": "2025-07-23", "Title": "Fitting Single and Mixture of Generalised Lambda Distributions", "Authors@R": "c(person(\"Steve\", \"Su\", role = c(\"aut\", \"cre\",\"cph\"),\nemail = \"allegro.su@gmail.com\", comment=c(ORCID = \"0000-0002-3368-4926\")),\nperson(\"Martin\", \"Maechler\", role = \"aut\"),\nperson(\"Juha\", \"Karvanen\", role = \"aut\"),\nperson(\"Robert\", \"King\", role = \"aut\"),\nperson(\"Benjamin\", \"Dean\", role = \"ctb\"),\nperson(\"R\", \"Core Team\", role = \"aut\"))", "Maintainer": "Steve Su ", "Description": "The fitting algorithms considered in this package have two\nmajor objectives. One is to provide a smoothing device to fit\ndistributions to data using the weight and unweighted\ndiscretised approach based on the bin width of the histogram.\nThe other is to provide a definitive fit to the data set using\nthe maximum likelihood and quantile matching estimation. Other\nmethods such as moment matching, starship method, L moment\nmatching are also provided. Diagnostics on goodness of fit can\nbe done via qqplots, KS-resample tests and comparing mean,\nvariance, skewness and kurtosis of the data with the fitted\ndistribution. References include the following: Karvanen and\nNuutinen (2008) \"Characterizing the generalized lambda\ndistribution by L-moments\" ,\nKing and MacGillivray (1999) \"A starship method for fitting the\ngeneralised lambda distributions\"\n, Su (2005) \"A Discretized\nApproach to Flexibly Fit Generalized Lambda Distributions to\nData\" , Su (2007) \"Nmerical\nMaximum Log Likelihood Estimation for Generalized Lambda\nDistributions\" , Su (2007)\n\"Fitting Single and Mixture of Generalized Lambda Distributions\nto Data via Discretized and Maximum Likelihood Methods: GLDEX\nin R\" , Su (2009) \"Confidence\nIntervals for Quantiles Using Generalized Lambda Distributions\"\n, Su (2010) \"Chapter 14:\nFitting GLDs and Mixture of GLDs to Data using Quantile\nMatching Method\" , Su (2010) \"Chapter 15:\nFitting GLD to data using GLDEX 1.0.4 in R\"\n, Su (2015) \"Flexible Parametric Quantile\nRegression Model\" , Su (2021)\n\"Flexible parametric accelerated failure time\nmodel\".", "License": "GPL (>= 3)", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-20 09:31:02 UTC", "User": "root" }, "Author": "Steve Su [aut, cre, cph] (ORCID:\n), Martin Maechler\n[aut], Juha Karvanen [aut], Robert King [aut], Benjamin Dean\n[ctb], R Core Team [aut]", "Repository": "https://allegropiano.r-universe.dev", "Date/Publication": "2025-07-23 08:30:10 UTC", "RemoteUrl": "https://github.com/cran/GLDEX", "RemoteRef": "HEAD", "RemoteSha": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "MD5sum": "bc9720c4a2e3942734124bee86dc9a3c", "_user": "allegropiano", "_type": "src", "_file": "GLDEX_2.0.0.9.4.tar.gz", "_fileid": "6cf29c88f105db6b9a8cc0252971e17822c1bccf5a3fa9e12e49e548734ea73e", "_filesize": 512298, "_sha256": "6cf29c88f105db6b9a8cc0252971e17822c1bccf5a3fa9e12e49e548734ea73e", "_created": "2026-05-20T09:31:02.000Z", "_published": "2026-06-02T13:23:26.649Z", "_distro": "noble", "_jobs": [ { "job": 79080543230, "time": 142, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7106377325" }, { "job": 79080543457, "time": 115, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7106367221" }, { "job": 79080543734, "time": 114, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7106367215" }, { "job": 79080543147, "time": 127, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7106372186" }, { "job": 79080543219, "time": 148, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7106379364" }, { "job": 79080543789, "time": 173, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7106388615" }, { "job": 79080543167, "time": 111, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7106365430" }, { "job": 79080543236, "time": 184, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7106392434" }, { "job": 79080542512, "time": 159, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7106322645" }, { "job": 79080542095, "time": 93, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7358871384" }, { "job": 79080542909, "time": 92, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7106359113" }, { "job": 79080543130, "time": 85, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7106357081" }, { "job": 79080543283, "time": 135, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7106375149" } ], "_buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/GLDEX", "_commit": { "id": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "author": "Steve Su ", "committer": "cran-robot ", "message": "version 2.0.0.9.4\n", "time": 1753259410 }, "_maintainer": { "name": "Steve Su", "email": "allegro.su@gmail.com", "login": "allegropiano", "description": "", "uuid": 127990928, "orcid": "0000-0002-3368-4926" }, "_registered": true, "_dependencies": [ { "package": "cluster", "role": "Depends" }, { "package": "grDevices", "role": "Depends" }, { "package": "graphics", "role": "Depends" }, { "package": "stats", "role": "Depends" }, { "package": "spacefillr", "role": "Depends" } ], "_owner": "cran", "_selfowned": true, "_usedby": 3, "_updates": [ { "week": "2025-30", "n": 1 } ], "_tags": [ { "name": "2.0.0.9.4", "date": "2025-07-23" } ], "_stars": 0, "_contributors": [ { "user": "allegropiano", "count": 16, "uuid": 127990928 } ], "_userbio": { "uuid": 127990928, "type": "user", "name": "allegropiano" }, "_downloads": { "count": 388, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/GLDEX" }, "_mentions": 2, "_searchresults": 97, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/GLDEX.html", "manual.pdf" ], "_realowner": "allegropiano", "_cranurl": false, "_releases": [ { "version": "1.0.4.4", "date": "2012-10-24" }, { "version": "1.0.4.5", "date": "2013-12-03" }, { "version": "2.0.0.0", "date": "2014-03-06" }, { "version": "2.0.0.1", "date": "2014-11-01" }, { "version": "2.0.0.2", "date": "2014-12-23" }, { "version": "2.0.0.3", "date": "2015-07-04" }, { "version": "2.0.0.4", "date": "2016-07-27" }, { "version": "2.0.0.5", "date": "2016-12-26" }, { "version": "2.0.0.6", "date": "2019-08-23" }, { "version": "2.0.0.7", "date": "2020-02-05" }, { "version": "2.0.0.8", "date": "2022-05-12" }, { "version": "2.0.0.9", "date": "2022-05-17" }, { "version": "2.0.0.9.1", "date": "2022-10-18" }, { "version": "2.0.0.9.2", "date": "2022-10-24" }, { "version": "2.0.0.9.3", "date": "2023-08-21" }, { "version": "2.0.0.9.4", "date": "2025-07-23" } ], "_exports": [ "C_check_gld", "C_dgl", "C_mult_check_gld", "C_optim_fun3", "C_optim_fun3_v", "C_pgl", "C_q_fmkl_gld_minmax_check", "C_q_rs_gld_minmax_check", "dgl", "digitsBase", "fun.auto.bimodal.ml", "fun.auto.bimodal.pml", "fun.auto.bimodal.qs", "fun.auto.mm.fmkl", "fun.auto.perc.rs", "fun.beta", "fun.beta1", "fun.bimodal.fit.ml", "fun.bimodal.fit.pml", "fun.bimodal.init", "fun.check.gld", "fun.check.gld.multi", "fun.class.regime.bi", "fun.comp.moments.ml", "fun.comp.moments.ml.2", "fun.data.fit.hs", "fun.data.fit.hs.nw", "fun.data.fit.lm", "fun.data.fit.ml", "fun.data.fit.mm", "fun.data.fit.qs", "fun.diag.ks.g", "fun.diag.ks.g.bimodal", "fun.diag1", "fun.diag2", "fun.disc.estimation", "fun.fit.gl.v.lm", "fun.fit.gl.v.lma", "fun.fit.gl.v2a", "fun.fit.gl.v2a.nw", "fun.fit.gl.v2b", "fun.fit.gl.v2b.nw", "fun.fit.gl.v3", "fun.fit.gl.v3a", "fun.fit.gl.v3m", "fun.fit.gl.v4", "fun.fit.gl.v4a", "fun.fit.gl.v6", "fun.fit.gl.v6a", "fun.fmkl", "fun.fmkl.L30", "fun.fmkl.L40", "fun.fmkl.mm.min", "fun.fmkl.mm.sol", "fun.fmkl.mm.sol.alt", "fun.fmkl.nr", "fun.fmkl0", "fun.fmkla", "fun.fmklb", "fun.gen.qrn", "fun.lambda.percentile", "fun.Lm.gt.2.fmkl", "fun.Lm.gt.2.rs", "fun.lm.theo.gld", "fun.mApply", "fun.minmax.check.gld", "fun.moments", "fun.moments.bimodal", "fun.moments.r", "fun.nclass.e", "fun.percentile", "fun.plot.fit", "fun.plot.fit.bm", "fun.plot.many.gld", "fun.rawmoments", "fun.RMFMKL.hs", "fun.RMFMKL.hs.nw", "fun.RMFMKL.lm", "fun.RMFMKL.ml", "fun.RMFMKL.ml.m", "fun.RMFMKL.mm", "fun.RMFMKL.qs", "fun.RPRS.hs", "fun.RPRS.hs.nw", "fun.RPRS.lm", "fun.RPRS.ml", "fun.RPRS.ml.m", "fun.RPRS.mm", "fun.RPRS.qs", "fun.rs.nr", "fun.rs.perc.gradient", "fun.rs.perc.min", "fun.rs.perc.sol", "fun.rs.perc.sol.alt", "fun.rsb", "fun.simu.bimodal", "fun.theo.bi.mv.gld", "fun.theo.mv.gld", "fun.which.zero", "fun.zero.omit", "gl.check.lambda.alt", "gl.check.lambda.alt1", "histsu", "is.inf", "is.notinf", "ks.gof", "kurtosis", "Lcoefs", "Lmomcov", "Lmomcov_calc", "Lmoments", "Lmoments_calc", "optim.fun.bi.final", "optim.fun.lm", "optim.fun.qs", "optim.fun2", "optim.fun2.nw", "optim.fun3", "optim.fun3.C", "optim.fun3.C.m", "optim.fun4", "optim.fun5", "optim.fun6", "optim.fun7", "pgl", "pretty.su", "qdgl", "qgl", "qqplot.gld", "qqplot.gld.bi", "QUnif", "rgl", "sHalton", "skewness", "starship", "starship.adaptivegrid", "starship.obj", "t1lmoments", "which.na" ], "_help": [ { "page": "GLDEX.package", "title": "This package fits RS and FMKL generalised lambda distributions using various methods. It also provides functions for fitting bimodal distributions using mixtures of generalised lambda distributions.", "topics": [ "GLDEX-package", "GLDEX" ] }, { "page": "digitsBase", "title": "Digit/Bit Representation of Integers in any Base", "topics": [ "digitsBase" ] }, { "page": "fun.auto.bimodal.ml", "title": "Fitting mixture of generalied lambda distribtions to data using maximum likelihood estimation via the EM algorithm", "topics": [ "fun.auto.bimodal.ml" ] }, { "page": "fun.auto.bimodal.pml", "title": "Fitting mixture of generalied lambda distribtions to data using parition maximum likelihood estimation", "topics": [ "fun.auto.bimodal.pml" ] }, { "page": "fun.auto.bimodal.qs", "title": "Fitting mixtures of generalied lambda distribtions to data using quantile matching method", "topics": [ "fun.auto.bimodal.qs" ] }, { "page": "fun.bimodal.fit.ml", "title": "Finds the final fits using the maximum likelihood estimation for the bimodal dataset.", "topics": [ "fun.bimodal.fit.ml" ] }, { "page": "fun.bimodal.fit.pml", "title": "Finds the final fits using partition maximum likelihood estimation for the bimodal dataset.", "topics": [ "fun.bimodal.fit.pml" ] }, { "page": "fun.bimodal.init", "title": "Finds the initial values for optimisation in fitting the bimodal generalised lambda distribution.", "topics": [ "fun.bimodal.init" ] }, { "page": "fun.check.gld", "title": "Check whether the RS or FMKL/FKML GLD is a valid GLD for single values of L1, L2, L3 and L4", "topics": [ "fun.check.gld" ] }, { "page": "fun.check.gld.multi", "title": "Check whether the RS or FMKL/FKML GLD is a valid GLD for vectors of L1, L2, L3 and L4", "topics": [ "fun.check.gld.multi" ] }, { "page": "fun.class.regime.bi", "title": "Classifies data into two groups using a clustering regime.", "topics": [ "fun.class.regime.bi" ] }, { "page": "fun.comp.moments.ml", "title": "Compare the moments of the data and the fitted univariate generalised lambda distribution.", "topics": [ "fun.comp.moments.ml" ] }, { "page": "fun.comp.moments.ml.2", "title": "Compare the moments of the data and the fitted univariate generalised lambda distribution. Specialised funtion designed for RMFMKL.ML and STAR methods.", "topics": [ "fun.comp.moments.ml.2" ] }, { "page": "fun.data.fit.hs", "title": "Fit RS and FMKL generalised distributions to data using discretised approach with weights.", "topics": [ "fun.data.fit.hs" ] }, { "page": "fun.data.fit.hs.nw", "title": "Fit RS and FMKL generalised distributions to data using discretised approach without weights.", "topics": [ "fun.data.fit.hs.nw" ] }, { "page": "fun.data.fit.lm", "title": "Fit data using L moment matching estimation for RS and FMKL GLD", "topics": [ "fun.data.fit.lm" ] }, { "page": "fun.data.fit.ml", "title": "Fit data using RS, FMKL maximum likelihood estimation and the FMKL starship method.", "topics": [ "fun.data.fit.ml" ] }, { "page": "fun.data.fit.mm", "title": "Fit data using moment matching estimation for RS and FMKL GLD", "topics": [ "fun.data.fit.mm" ] }, { "page": "fun.data.fit.qs", "title": "Fit data using quantile matching estimation for RS and FMKL GLD", "topics": [ "fun.data.fit.qs" ] }, { "page": "fun.diag.ks.g", "title": "Compute the simulated Kolmogorov-Smirnov tests for the unimodal dataset", "topics": [ "fun.diag.ks.g" ] }, { "page": "fun.diag.ks.g.bimodal", "title": "Compute the simulated Kolmogorov-Smirnov tests for the bimodal dataset", "topics": [ "fun.diag.ks.g.bimodal" ] }, { "page": "fun.diag1", "title": "Diagnostic function for theoretical distribution fits through the resample Kolmogorov-Smirnoff tests", "topics": [ "fun.diag1" ] }, { "page": "fun.diag2", "title": "Diagnostic function for empirical data distribution fits through the resample Kolmogorov-Smirnoff tests", "topics": [ "fun.diag2" ] }, { "page": "fun.disc.estimation", "title": "Estimates the mean and variance after cutting up a vector of variable into evenly spaced categories.", "topics": [ "fun.disc.estimation" ] }, { "page": "fun.gen.qrn", "title": "Finds the low discrepancy quasi random numbers", "topics": [ "fun.gen.qrn" ] }, { "page": "fun.lm.theo.gld", "title": "Find the theoretical first four L moments of the generalised lambda distribution.", "topics": [ "fun.lm.theo.gld" ] }, { "page": "fun.mApply", "title": "Applying functions based on an index for a matrix.", "topics": [ "fun.mApply" ] }, { "page": "fun.minmax.check.gld", "title": "Check whether the specified GLDs cover the minimum and the maximum values in a dataset", "topics": [ "fun.minmax.check.gld" ] }, { "page": "fun.moments.bimodal", "title": "Finds the moments of fitted mixture of generalised lambda distribution by simulation.", "topics": [ "fun.moments.bimodal" ] }, { "page": "fun.moments.r", "title": "Calculate mean, variance, skewness and kurtosis of a numerical vector", "topics": [ "fun.moments.r" ] }, { "page": "fun.nclass.e", "title": "Estimates the number of classes or bins to smooth over in the discretised method of fitting generalised lambda distribution to data.", "topics": [ "fun.nclass.e" ] }, { "page": "fun.plot.fit", "title": "Plotting the univariate generalised lambda distribution fits on the data set.", "topics": [ "fun.plot.fit" ] }, { "page": "fun.plot.fit.bm", "title": "Plotting mixture of two generalised lambda distributions on the data set.", "topics": [ "fun.plot.fit.bm" ] }, { "page": "fun.plot.many.gld", "title": "Plotting many univariate generalised lambda distributions on one page.", "topics": [ "fun.plot.many.gld" ] }, { "page": "fun.rawmoments", "title": "Computes the raw moments of the generalised lambda distribution up to 4th order.", "topics": [ "fun.rawmoments" ] }, { "page": "fun.RMFMKL.hs", "title": "Fit FMKL generalised distribution to data using discretised approach with weights.", "topics": [ "fun.RMFMKL.hs" ] }, { "page": "fun.RMFMKL.hs.nw", "title": "Fit FMKL generalised distribution to data using discretised approach without weights.", "topics": [ "fun.RMFMKL.hs.nw" ] }, { "page": "fun.RMFMKL.lm", "title": "Fit FMKL generalised lambda distribution to data set using L moment matching", "topics": [ "fun.RMFMKL.lm" ] }, { "page": "fun.RMFMKL.ml", "title": "Fit FMKL generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RMFMKL.ml" ] }, { "page": "fun.RMFMKL.ml.m", "title": "Fit RS generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RMFMKL.ml.m" ] }, { "page": "fun.RMFMKL.mm", "title": "Fit FMKL generalised lambda distribution to data set using moment matching", "topics": [ "fun.RMFMKL.mm" ] }, { "page": "fun.RMFMKL.qs", "title": "Fit FMKL generalised lambda distribution to data set using quantile matching", "topics": [ "fun.RMFMKL.qs" ] }, { "page": "fun.RPRS.hs", "title": "Fit RS generalised distribution to data using discretised approach with weights.", "topics": [ "fun.RPRS.hs" ] }, { "page": "fun.RPRS.hs.nw", "title": "Fit RS generalised distribution to data using discretised approach without weights.", "topics": [ "fun.RPRS.hs.nw" ] }, { "page": "fun.RPRS.lm", "title": "Fit RS generalised lambda distribution to data set using L moment matching", "topics": [ "fun.RPRS.lm" ] }, { "page": "fun.RPRS.ml", "title": "Fit RS generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RPRS.ml" ] }, { "page": "fun.RPRS.ml.m", "title": "Fit RS generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RPRS.ml.m" ] }, { "page": "fun.RPRS.mm", "title": "Fit RS generalised lambda distribution to data set using moment matching", "topics": [ "fun.RPRS.mm" ] }, { "page": "fun.RPRS.qs", "title": "Fit RS generalised lambda distribution to data set using quantile matching", "topics": [ "fun.RPRS.qs" ] }, { "page": "fun.simu.bimodal", "title": "Simulate a mixture of two generalised lambda distributions.", "topics": [ "fun.simu.bimodal" ] }, { "page": "fun.theo.bi.mv.gld", "title": "Calculates the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.", "topics": [ "fun.theo.bi.mv.gld" ] }, { "page": "fun.theo.mv.gld", "title": "Find the theoretical first four moments of the generalised lambda distribution.", "topics": [ "fun.theo.mv.gld" ] }, { "page": "fun.which.zero", "title": "Determine which values are zero.", "topics": [ "fun.which.zero" ] }, { "page": "fun.zero.omit", "title": "Returns a vector after removing all the zeros.", "topics": [ "fun.zero.omit" ] }, { "page": "gl.check.lambda.alt", "title": "Checks whether the parameters provided constitute a valid generalised lambda distribution.", "topics": [ "gl.check.lambda.alt" ] }, { "page": "gl.check.lambda.alt1", "title": "Checks whether the parameters provided constitute a valid generalised lambda distribution.", "topics": [ "gl.check.lambda.alt1" ] }, { "page": "GLDfunctions", "title": "The Generalised Lambda Distribution Family", "topics": [ "dgl", "pgl", "qdgl", "qgl", "rgl" ] }, { "page": "histsu", "title": "Histogram with exact number of bins specified by the user", "topics": [ "histsu" ] }, { "page": "is.inf", "title": "Returns a logical vecto, TRUE if the value is Inf or -Inf.", "topics": [ "is.inf" ] }, { "page": "is.notinf", "title": "Returns a logical vector TRUE, if the value is not Inf or -Inf.", "topics": [ "is.notinf" ] }, { "page": "ks.gof", "title": "Kolmogorov-Smirnov test", "topics": [ "ks.gof" ] }, { "page": "Lmoments", "title": "L-moments", "concept": [ "moments", "skewness", "kurtosis", "descriptive statistics" ], "topics": [ "Lcoefs", "Lmomcov", "Lmomcov_calc", "Lmoments", "Lmoments_calc" ] }, { "page": "pretty.su", "title": "An alternative to the normal pretty function in R.", "topics": [ "pretty.su" ] }, { "page": "qqplot.gld", "title": "Do a quantile plot on the univariate distribution fits.", "topics": [ "qqplot.gld" ] }, { "page": "qqplot.gld.bi", "title": "Do a quantile plot on the bimodal distribution fits.", "topics": [ "qqplot.gld.bi" ] }, { "page": "QUnif", "title": "Quasi Randum Numbers via Halton Sequences", "concept": [ "Quasi Monte Carlo", "low discrepancy sequence", "space filling" ], "topics": [ "QUnif", "sHalton" ] }, { "page": "skewnessandkurtosis", "title": "Compute skewness and kurtosis statistics", "topics": [ "kurtosis", "skewness" ] }, { "page": "starship", "title": "Carry out the ``starship'' estimation method for the generalised lambda distribution", "topics": [ "starship" ] }, { "page": "starship.adaptivegrid", "title": "Carry out the ``starship'' estimation method for the generalised lambda distribution using a grid-based search", "topics": [ "starship.adaptivegrid" ] }, { "page": "starship.obj", "title": "Objective function that is minimised in starship estimation method", "topics": [ "starship.obj" ] }, { "page": "t1lmoments", "title": "Trimmed L-moments", "concept": [ "moments", "skewness", "kurtosis", "descriptive statistics", "heavy tails" ], "topics": [ "t1lmoments" ] }, { "page": "which.na", "title": "Determine Missing Values", "topics": [ "which.na" ] } ], "_rundeps": [ "cluster", "Rcpp", "spacefillr" ], "_score": 3.2420442393695508, "_indexed": true, "_nocasepkg": "gldex", "_universes": [ "allegropiano" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:33.000Z", "distro": "noble", "arch": "aarch64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "b3c363d1e99a514b5f25aca3efa9bff252d1892e9aeeeef8b1defa9c3cffa732", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.7.0", "os": "linux", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:02.000Z", "distro": "noble", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "f01aaedd8b5c01c4989c8637903ac58205a1d661cce68fb126b56e19ac4585f8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.6.0", "os": "linux", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:06.000Z", "distro": "noble", "arch": "aarch64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "3a218fb072f81871510ee3a110806a05703b47b644d0c9f705c81ffef1926748", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.6.0", "os": "linux", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:16.000Z", "distro": "noble", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "7ee5bbd92213f5b3822a8b24ce342af4c42c0387f25e6b0a0fa6e7d6f1c89bfa", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.5.3", "os": "mac", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:35.000Z", "arch": "aarch64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "0f6d0809d7d6cf78137d84883f078284b2e2031c20f5930fb374762c075a2556", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.5.3", "os": "mac", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:50.000Z", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "847177bfc4de63cb45abc4e7db87b588ab96720ca828f53ff253790e95646cbd", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.6.0", "os": "mac", "version": "2.0.0.9.4", "date": "2026-05-20T09:32:56.000Z", "arch": "aarch64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "12b81a40c39b4b8f1319a21e9e84c5b8c7c7b0c24b724528e82453e079599618", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.6.0", "os": "mac", "version": "2.0.0.9.4", "date": "2026-05-20T09:33:57.000Z", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "92d409c546bb768743dd8d61fa4c04c1542fe2ab32dbf3d0cdf8c0109d4330c8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.7.0", "os": "win", "version": "2.0.0.9.4", "date": "2026-05-20T09:32:26.000Z", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "c02a8e139b596941e56cce393ad131f11249daec74d7ae6d0dad993bb5f20eec", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.5.3", "os": "win", "version": "2.0.0.9.4", "date": "2026-05-20T09:32:17.000Z", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "2054afe47d78d0bf692cd335597585c1f255ece7d176f771955b40a083ff1e8c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.6.0", "os": "win", "version": "2.0.0.9.4", "date": "2026-05-20T09:32:55.000Z", "arch": "x86_64", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "c49b5b34ebafee49277d5b93bb2c3e6c70ff096551e8aac3f42b17549520f025", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" }, { "r": "4.6.0", "os": "wasm", "version": "2.0.0.9.4", "date": "2026-06-02T13:21:56.000Z", "arch": "emscripten", "commit": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "fileid": "bdbd4e54909eb7c8b907bcf3560389f24fbd3ea9fb69073d48b4131e7b3f16a2", "status": "success", "buildurl": "https://github.com/r-universe/allegropiano/actions/runs/26153814766" } ] }