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{confintr} \u003ca href='https://github.com/mayer79/confintr'\u003e\u003cimg src='man/figures/logo.png' align=\"right\" height=\"139\"/\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n\n[![R-CMD-check](https://github.com/mayer79/confintr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/mayer79/confintr/actions/actions/workflows/R-CMD-check.yaml)\n[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/confintr)](https://cran.r-project.org/package=confintr)\n[![Codecov test coverage](https://codecov.io/gh/mayer79/confintr/graph/badge.svg)](https://app.codecov.io/gh/mayer79/confintr)\n\n[![](https://cranlogs.r-pkg.org/badges/confintr)](https://cran.r-project.org/package=confintr) \n[![](https://cranlogs.r-pkg.org/badges/grand-total/confintr?color=orange)](https://cran.r-project.org/package=confintr)\n\n\u003c!-- badges: end --\u003e\n\n## Overview\n\n{confintr} offers classic and/or bootstrap confidence intervals (CI) for the following parameters:\n\n- mean,\n- quantiles incl. median,\n- proportion,\n- variance and standard deviation,\n- IQR and MAD,\n- skewness and kurtosis,\n- R-squared and the non-centrality parameter of the F distribution,\n- Cramér's V and the non-centrality parameter of the chi-squared distribution,\n- odds ratio of a 2x2 table,\n- Pearson-, Spearman-, Kendall correlation coefficients,\n- mean differences, quantile and median differences.\n\nBoth one- and two-sided intervals are supported.\n\nDifferent types of bootstrap intervals are available via {boot}, see vignette.\n\n## Installation\n\n```r\n# From CRAN\ninstall.packages(\"confintr\")\n\n# Development version\ndevtools::install_github(\"mayer79/confintr\")\n```\n\n## Usage\n\n``` r\nlibrary(confintr)\nset.seed(1)\n\n# Mean\nci_mean(1:100)\n\n# Two-sided 95% t confidence interval for the population mean\n# \n# Sample estimate: 50.5 \n# Confidence interval:\n#     2.5%    97.5% \n# 44.74349 56.25651 \n\n# Mean using the Bootstrap\nci_mean(1:100, type = \"bootstrap\")\n\n#   Two-sided 95% bootstrap confidence interval for the population mean\n# \tbased on 9999 bootstrap replications and the student method\n# \n# Sample estimate: 50.5 \n# Confidence interval:\n#     2.5%    97.5% \n# 44.72913 56.34685\n\n# 95% value at risk\nci_quantile(rexp(1000), q = 0.95)\n\n# \tTwo-sided 95% binomial confidence interval for the population 95%\n# \tquantile\n# \n# Sample estimate: 2.954119 \n# Confidence interval:\n#     2.5%    97.5% \n# 2.745526 3.499928 \n\n# Mean difference\nci_mean_diff(1:100, 2:101)\n\n#\tTwo-sided 95% t confidence interval for the population value of mean(x)-mean(y)\n#\n# Sample estimate: -1 \n# Confidence interval:\n#      2.5%     97.5% \n# -9.090881  7.090881 \n\nci_mean_diff(1:100, 2:101, type = \"bootstrap\", seed = 1)\n\n# Two-sided 95% bootstrap confidence interval for the population value of mean(x)-mean(y)\n# based on 9999 bootstrap replications and the student method\n#\n# Sample estimate: -1 \n# Confidence interval:\n#      2.5%     97.5% \n# -9.057506  7.092050\n\n# Further examples (without output)\n\n# Correlation\nci_cor(iris[1:2], method = \"spearman\", type = \"bootstrap\")\n\n# Proportions\nci_proportion(10, n = 100, type = \"Wilson\")\nci_proportion(10, n = 100, type = \"Clopper-Pearson\")\n\n# R-squared\nfit \u003c- lm(Sepal.Length ~ ., data = iris)\nci_rsquared(fit, probs = c(0.05, 1))\n\n# Kurtosis\nci_kurtosis(1:100)\n\n# Mean difference\nci_mean_diff(10:30, 1:15)\nci_mean_diff(10:30, 1:15, type = \"bootstrap\")\n\n# Median difference\nci_median_diff(10:30, 1:15)\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmayer79%2Fconfintr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmayer79%2Fconfintr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmayer79%2Fconfintr/lists"}