{ "_id": "6a153be7acfb0bcc41d52dfa", "Package": "logcondens", "Type": "Package", "Title": "Estimate a Log-Concave Probability Density from Iid Observations", "Version": "2.1.9", "Date": "2026-04-26", "Authors@R": "c(person(given = \"Kaspar\", family = \"Rufibach\", role = c(\"aut\", \"cre\"), email = \"kaspar.rufibach@gmail.com\"),\nperson(given = \"Duembgen\", family = \"Lutz\", role = \"aut\", email = \"duembgen@stat.unibe.ch\"))", "Maintainer": "Kaspar Rufibach ", "Description": "Given independent and identically distributed observations\nX(1), ..., X(n), compute the maximum likelihood estimator (MLE)\nof a density as well as a smoothed version of it under the\nassumption that the density is log-concave, see Rufibach (2007)\nand Duembgen and Rufibach (2009). The main function of the\npackage is 'logConDens' that allows computation of the\nlog-concave MLE and its smoothed version. In addition, we\nprovide functions to compute (1) the value of the density and\ndistribution function estimates (MLE and smoothed) at a given\npoint (2) the characterizing functions of the estimator, (3) to\nsample from the estimated distribution, (5) to compute a\ntwo-sample permutation test based on log-concave densities, (6)\nthe ROC curve based on log-concave estimates within cases and\ncontrols, including confidence intervals for given values of\nfalse positive fractions (7) computation of a confidence\ninterval for the value of the true density at a fixed point.\nFinally, three datasets that have been used to illustrate\nlog-concave density estimation are made available.", "License": "GPL (>= 2)", "URL": "http://www.kasparrufibach.ch ,\nhttps://www.imsv.unibe.ch/about_us/staff/prof_dr_duembgen_lutz/index_eng.html", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 06:13:30 UTC", "User": "root" }, "Author": "Kaspar Rufibach [aut, cre], Duembgen Lutz [aut]", "Repository": 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