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{confintr} offers classic and/or bootstrap confidence intervals (CI) for the following parameters:

- mean,
- quantiles incl. median,
- proportion,
- variance and standard deviation,
- IQR and MAD,
- skewness and kurtosis,
- R-squared and the non-centrality parameter of the F distribution,
- CramÃ©r's V and the non-centrality parameter of the chi-squared distribution,
- odds ratio of a 2x2 table,
- Pearson-, Spearman-, Kendall correlation coefficients,
- mean differences, quantile and median differences.

Both one- and two-sided intervals are supported.

Different types of bootstrap intervals are available via {boot}, see vignette.

```
# From CRAN
install.packages("confintr")
# Development version
devtools::install_github("mayer79/confintr")
```

```
library(confintr)
set.seed(1)
# Mean
ci_mean(1:100)
# Two-sided 95% t confidence interval for the population mean
#
# Sample estimate: 50.5
# Confidence interval:
# 2.5% 97.5%
# 44.74349 56.25651
# Mean using the Bootstrap
ci_mean(1:100, type = "bootstrap")
# Two-sided 95% bootstrap confidence interval for the population mean
# based on 9999 bootstrap replications and the student method
#
# Sample estimate: 50.5
# Confidence interval:
# 2.5% 97.5%
# 44.72913 56.34685
# 95% value at risk
ci_quantile(rexp(1000), q = 0.95)
# Two-sided 95% binomial confidence interval for the population 95%
# quantile
#
# Sample estimate: 2.954119
# Confidence interval:
# 2.5% 97.5%
# 2.745526 3.499928
# Mean difference
ci_mean_diff(1:100, 2:101)
# Two-sided 95% t confidence interval for the population value of mean(x)-mean(y)
#
# Sample estimate: -1
# Confidence interval:
# 2.5% 97.5%
# -9.090881 7.090881
ci_mean_diff(1:100, 2:101, type = "bootstrap", seed = 1)
# Two-sided 95% bootstrap confidence interval for the population value of mean(x)-mean(y)
# based on 9999 bootstrap replications and the student method
#
# Sample estimate: -1
# Confidence interval:
# 2.5% 97.5%
# -9.057506 7.092050
# Further examples (without output)
# Correlation
ci_cor(iris[1:2], method = "spearman", type = "bootstrap")
# Proportions
ci_proportion(10, n = 100, type = "Wilson")
ci_proportion(10, n = 100, type = "Clopper-Pearson")
# R-squared
fit <- lm(Sepal.Length ~ ., data = iris)
ci_rsquared(fit, probs = c(0.05, 1))
# Kurtosis
ci_kurtosis(1:100)
# Mean difference
ci_mean_diff(10:30, 1:15)
ci_mean_diff(10:30, 1:15, type = "bootstrap")
# Median difference
ci_median_diff(10:30, 1:15)
```

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