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SSBtools - Algorithms and Tools for Tabular Statistics and Hierarchical Computations

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

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statistics

7.75 score 12 stars 7 dependents 80 scripts 1.0k downloads

GaussSuppression - Tabular Data Suppression using Gaussian Elimination

A statistical disclosure control tool to protect tables by suppression using the Gaussian elimination secondary suppression algorithm (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6>. A suggestion is to start by working with functions SuppressSmallCounts() and SuppressDominantCells(). These functions use primary suppression functions for the minimum frequency rule and the dominance rule, respectively. Novel functionality for suppression of disclosive cells is also included. General primary suppression functions can be supplied as input to the general working horse function, GaussSuppressionFromData(). Suppressed frequencies can be replaced by synthetic decimal numbers as described in Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.

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7.30 score 4 stars 62 scripts 340 downloads

PxWebApiData - PX-Web Data by API

Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.

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6.58 score 5 stars 128 scripts 446 downloads

easySdcTable - Easy Interface to the Statistical Disclosure Control Package 'sdcTable' Extended with Own Implementation of 'GaussSuppression'

The main function, ProtectTable(), performs table suppression according to a frequency rule with a data set as the only required input. Within this function, protectTable(), protect_linked_tables() or runArgusBatchFile() in package 'sdcTable' is called. Lists of level-hierarchy (parameter 'dimList') and other required input to these functions are created automatically. The suppression method Gauss (default) is implemented independently of 'sdcTable'. The function, PTgui(), starts a graphical user interface based on the 'shiny' package.

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4.73 score 1 stars 18 scripts 340 downloads

SmallCountRounding - Small Count Rounding of Tabular Data

A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.

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statistics

4.73 score 3 stars 18 scripts 303 downloads

RegSDC - Information Preserving Regression-Based Tools for Statistical Disclosure Control

Implementation of the methods described in the paper with the above title: Langsrud, Ø. (2019) <doi:10.1007/s11222-018-9848-9>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.

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3.48 score 1 stars 1 dependents 20 scripts 306 downloads

CalibrateSSB - Weighting and Estimation for Panel Data with Non-Response

Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the 'survey' package is used for this purpose. It is also possible to use 'ReGenesees', which can be installed from <https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) <http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.

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3.08 score 2 stars 12 scripts 629 downloads

ffmanova - Fifty-Fifty MANOVA

General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.

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3.00 score 2 stars 7 scripts 657 downloads