Maintainer: Luca Sartore

*This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy.*

Calibration forces the weighted estimates of calibration variables to match known totals. This improves the quality of the design-weighted estimates. It is used to adjust for non-response and/or under-coverage. The commonly used methods of calibration produce non-integer weights. In cases where weighted estimates must be integers, one must “integerize” the calibrated weights. However, this procedure often produces final weights that are very different for the “sample” weights. To counter this problem, the **inca** package provides specific functions for rounding real weights to integers, and performing an integer programming algorithm for calibration problems with integer weights.

For a complete list of exported functions, use `library(help = "inca")`

once the **inca** package is installed (see the `inst/INSTALL.md`

file for a detailed description of the setup process).

```
library(inca)
set.seed(0)
w <- rpois(150, 4)
data <- matrix(rbinom(150000, 1, .3) * rpois(150000, 4), 1000, 150)
y <- data %*% w
w <- runif(150, 0, 7.5)
print(sum(abs(y - data %*% w)))
cw <- intcalibrate(w, ~. + 0, y, lower = 1, upper = 7, sparse = TRUE, data = data)
print(sum(abs(y - data %*% cw)))
barplot(table(cw), main = "Calibrated integer weights")
```

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Sartore, L., Toppin, K., Young, L., Spiegelman, C. 2019. Developing integer calibration weights for the Census of Agriculture. *Journal of Agricultural, Biological, and Environmental Statistics*, **24**(1), 26-48.