https://github.com/inseefr/icarus
A package with useful functions for calibration and reweighting in survey sampling
https://github.com/inseefr/icarus
calibration icarus sampling stats survey-sampling
Last synced: about 1 month ago
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A package with useful functions for calibration and reweighting in survey sampling
- Host: GitHub
- URL: https://github.com/inseefr/icarus
- Owner: InseeFr
- Created: 2015-07-10T09:27:17.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2025-09-05T13:43:40.000Z (7 months ago)
- Last Synced: 2025-09-05T15:37:09.109Z (7 months ago)
- Topics: calibration, icarus, sampling, stats, survey-sampling
- Language: R
- Homepage:
- Size: 14.2 MB
- Stars: 10
- Watchers: 3
- Forks: 7
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
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# Icarus
Icarus (Icarus Calibrates And Reweights Units in Samples) is an R package providing useful functions for calibration and reweighting estimators in survey sampling. The former name of this package was gaston.
## Cite
To cite Icarus in publications use: Rebecq, Antoine (2017). Icarus: an R package for calibration in survey sampling. R package version 0.3.0.
## Install
You can use the following instruction to install icarus (from CRAN):
```
install.packages("icarus")
```
However, if you wish to install the latest version of icarus, you can use devtools and install directly from this github repo:
```
install.packages("devtools")
library(devtools)
install_github("haroine/icarus")
````
## Short example
In this example, we perform calibration (with the "raking" method) on the test dataset _data_employees_ included in icarus:
```
library(icarus)
N <- 300 ## Population size
## Compute the Horvitz-Thompson estimator (returns 1.666667)
weightedMean(data_employees$movies, data_employees$weight, N)
## Add calibration margins
mar1 <- c("category",3,80,90,60)
mar2 <- c("sex",2,140,90,0)
mar3 <- c("department",2,100,130,0)
mar4 <- c("salary", 0, 470000,0,0)
margins <- rbind(mar1, mar2, mar3, mar4)
## Compute calibration weights
wCal <- calibration(data=data_employees, marginMatrix=margins, colWeights="weight"
, method="raking", description=FALSE)
## Value of the calibrated estimator: 2.471917
weightedMean(data_employees$movies, wCal, N)
```