https://github.com/mittelmark/snha
St. Nicolas House Algorithm implementation in R - predicting correlation networks using association chains
https://github.com/mittelmark/snha
correlation-analysis network network-analysis network-reconstruction r-package
Last synced: 4 months ago
JSON representation
St. Nicolas House Algorithm implementation in R - predicting correlation networks using association chains
- Host: GitHub
- URL: https://github.com/mittelmark/snha
- Owner: mittelmark
- License: other
- Created: 2023-02-12T20:53:43.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-12-11T12:18:09.000Z (6 months ago)
- Last Synced: 2025-12-12T11:52:31.832Z (6 months ago)
- Topics: correlation-analysis, network, network-analysis, network-reconstruction, r-package
- Language: R
- Homepage:
- Size: 176 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
- License: LICENSE
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# Snha package
R package which implements the St. Nicolas House Algorithm (SNHA) for
constructing networks of correlated variables using a ranking of the pairwise
correlation values. The package contains the R code for the papers:
- Groth, D., Scheffler, C., & Hermanussen, M. (2019). Body height in stunted
Indonesian children depends directly on parental education and not via
a nutrition mediated pathway-Evidence from tracing association chains by St.
Nicolas House Analysis. Anthropologischer Anzeiger, 76(5), 445-451.
[https://doi.org/10.1127/anthranz/2019/1027](https://doi.org/10.1127/anthranz/2019/1027)
- Hermanussen, M., Aßmann, C., & Groth, D. (2021). Chain Reversion for Detecting
Associations in Interacting Variables—St. Nicolas House Analysis.
International journal of environmental research and public health, 18(4), 1741
[https://doi.org/10.3390/ijerph18041741](https://doi.org/10.3390/ijerph18041741)
For an implementation of the algorithm in Python look here
[https://github.com/thake93/snha4py](https://github.com/thake93/snha4py).
## CRAN Statistics
* Downloads - total: 
* Downloads - monthly: 
* Downloads - weekly: 
## Installation
Either install the package directly from CRAN as usually:
```
install.packages('snha')
```
For installing the latest stable version directly from Github you can execute the following command in your R console:
```
install.packages(
"https://github.com/mittelmark/snha/releases/download/v0.2.1/snha_0.2.1.tar.gz",
repos=NULL);
```
Thereafter you can load the package and the vignette of the package like this:
```
library(snha)
vignette("tutorial",package="snha)
citation("snha")
```
Or to use the latest development version from the Github repository install it like this:
```
library(remotes)
remotes::install_github("https://github.com/mittelmark/snha")
```
## Example
The package has a function `snha` where you give your data as input. The
function creates an object of class `snha` which you can plot and
explore easily. Here an example just using the `swiss` data which are part of
every R installation:
```r
> library(snha)
> library(MASS)
> data(swiss)
> colnames(swiss)=abbreviate(swiss)
> as=snha(swiss,method="spearman")
> plot(as)
> plot(as,layout="sam",vertex.size=8)
> ls(as)
[1] "alpha" "chains" "data" "method"
[5] "p.values" "probabilities" "sigma" "theta"
[9] "threshold"
> as$theta
Frtl Agrc Exmn Edct Cthl In.M
Frtl 0 0 1 0 0 1
Agrc 0 0 0 1 0 0
Exmn 1 0 0 1 1 0
Edct 0 1 1 0 0 0
Cthl 0 0 1 0 0 0
In.M 1 0 0 0 0 0
```
The theta object contains the adjacency matrix with the edges for the found
graph. For more details consult the package vignette:
`vignette(package="snha","tutorial")` or the manual package of the package
`?snha` or `?'snha-package'`.
## Author and Copyright
Author: Detlef Groth, University of Potsdam, Germany
License: MIT License see the file [LICENSE](LICENSE) for details.
## Contributors
The following persons have contributed to the package with ideas, testing, etc.:
* Michael Hermanussen (Aschauhof, the algorithm idea)
* Masiar Novine (University of Potsdam, evaluating different data generation methods and comparing the algorithm swit other approaches)
* Tim Hake (University of Potsdam, Python port and critical discussions about directed edges)
* Bernhard Bodenberger (University of Potsdam, evaluating the bootstrap performances and different parts of the algorithm)
* Cedric Moris (University of Potsdam, evaluating different extensions of the basic algorithm)
## Bug reporting
In case of bugs and suggestions, use the [issues](https://github.com/mittelmark/snha/issues) link on top.