EVER (Estimation of Variance by Efficient Replication)

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The contents related to EVER are shown in the following sections:


The EVER package (the acronym stands for: Estimation of Variance by Efficient Replication) is mainly intended for calculating estimates and standard errors in complex surveys. Variance estimation is based on the extended DAGJK (Delete-A-group Jackknife) technique proposed by P. S. Kott.

The advantage of the DAGJK method over the traditional jackknife is that, unlike the latter, it remains computationally manageable even when dealing with “complex and big” surveys (tens of thousands of PSUs arranged in a large number of strata with widely varying sizes). In fact, the DAGJK method is known to provide, for a broad range of sampling designs and estimators, (near) unbiased standard error estimates even with a “small” number (e.g. a few tens) of replicate weights.

Besides its peculiar computational efficiency, the DAGJK method takes advantage of the strong points it shares with the most common replication methods. As a remarkable example, EVER is designed to fully exploit DAGJK’s versatility: the package provides the user with a user-friendly tool for calculating estimates, standard errors and confidence intervals for estimators defined by the user themselves (even non-analytic). This functionality makes EVER especially appealing whenever variance estimation by Taylor linearisation can be applied only at the price of crude approximations (e.g. poverty estimates).

Main Statistical Functions

  • Delete-A-Group Jackknife replication
  • Calibration of replicate weights
  • Estimates and Sampling Errors (standard error, variance, coefficient of variation, confidence interval, design effect) for:
    • Totals
    • Means
    • Absolute and relative frequency distributions
    • Ratios between totals
    • Multiple regression coefficients
    • Quantiles
  • Estimates and Sampling Errors for user-defined Complex Estimators (even non-analytic)
  • Estimates and Sampling Errors for Subpopulations (Domains)
    • All the analyses above can be carried out for arbitrary domains


Status: validated
Author: Istat
Licence: EUPL-1.1
GSBPM code: 5.6 Calculate weights
5.7 Calculate aggregates
Programming language: R
Keywords: calibration, estimation, replication variance, complex surveys, complex estimators, Delete-A-Group Jackknife, R
Contact: name: Diego Zardetto
email: zardetto@istat.it

Software and documentation


R (version ≥ 2.5.1)


Copyright 2013 Istat

Licensed under the European Union Public Licence (EUPL), version 1.1 or subsequent. You may not use this work except in compliance with the Licence. You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl.html. Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an “AS IS” basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the Licence for the specific language governing permissions and limitations under the Licence.


Istat assumes no responsibility for the results arising from use of the instrument that is inconsistent with the methodological guidance contained in the documentation available.

Release date: 16/06/2020


Install the downloaded package from within R as follows:
> install.packages(path_to_file, repos = NULL)
where the character path_to_file is the path to the .zip or .tar.gz file you downloaded.


Reference manual – EVER  v. 1.3


EVER website

Last edit: 02 December 2021