{ "_id": "6a1e8c021d7bb097a0a694a0", "Package": "LearningStats", "Type": "Package", "Title": "Elemental Descriptive and Inferential Statistics", "Version": "0.1.0", "Authors@R": "c(\nperson(\"María Isabel\", \"Borrajo-García\", role = c(\"aut\", \"cre\"),\nemail = \"mariaisabel.borrajo@usc.es\"),\nperson(\"Mercedes\", \"Conde-Amboage\", role = \"aut\",\nemail = \"mercedes.amboage@usc.es\"),\nperson(\"Alejandra\", \"López-Pérez\", role = \"aut\",\nemail = \"alejandralopez.perez@usc.es\")\n)", "Description": "Provides tools to teach students elemental statistics. The\nmain topics covered are descriptive statistics, probability\nmodels (discrete and continuous variables) and statistical\ninference (confidence intervals and hypothesis tests). One of\nthe main advantages of this package is that allows the user to\nread quite a variety of types of data files with one unique\ncommand. Moreover it includes shortcuts to simple but up-to-now\nnot in R descriptive features such a complete frequency table\nor an histogram with the optimal number of intervals. Related\nto model distributions (both discrete and continuous), the\npackage allows the student to easy plot the mass/density\nfunction, distribution function and quantile function just\ndetailing as input arguments the known population parameters.\nThe inference related tools are basically confidence interval\nand hypothesis testing. Having defined independent commands for\nthese two tools makes it easier for the student to understand\nwhat the software is performing, and it also helps the student\nto have a better knowledge on which specific tool they need to\nuse in each situation. Moreover, the hypothesis testing\ncommands provide not only the numeric result on the screen but\nalso a very intuitive graph (which includes the statistic\ndistribution, the observed value of the statistic, the\nrejection area and the p-value) that is very useful for the\nstudent to visualise the process. The regression section\nincludes up to now, a simple linear model, with one single\ncommand the student can obtain the numeric summary as well as\nthe corresponding diagram with the adjusted regression model\nand a legend with basic information (formula of the adjusted\nmodel and R-squared).", "Language": "en-GB", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.1.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 07:49:35 UTC", "User": "root" }, "Author": "María Isabel Borrajo-García [aut, cre], Mercedes Conde-Amboage\n[aut], Alejandra López-Pérez [aut]", "Maintainer": "María Isabel Borrajo-García ", "Config/pak/sysreqs": "make libicu-dev libx11-dev zlib1g-dev", "Repository": "https://maribelborrajo.r-universe.dev", "Date/Publication": "2021-04-21 06:10:05 UTC", "RemoteUrl": "https://github.com/cran/LearningStats", "RemoteRef": "HEAD", "RemoteSha": "df5f6a1dfe0bd8922b2f5501054c963cf88e05b4", "MD5sum": "9882f78a9bc6d2d4c28d3e5f42501c56", 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