confintr

The confintr package offers classic and/or bootstrap confidence intervals 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,

• Cramér's V and the non-centrality parameter of the chi-squared distribution,

• the odds ratio of a 2x2 table,

• Pearson-, Spearman-, Kendall correlation coefficients,

• mean, quantile and median differences.

Both one- and two-sided intervals are supported.

Different types of bootstrap intervals are possible through argument boot_type, see vignette.

Installation

From CRAN:

install.packages("confintr")

Latest version from github:

library(devtools)
install_github("mayer79/confintr")

Teaser

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", R  = 999)

# Median difference
ci_median_diff(10:30, 1:15, R  = 999)