Package: SEset 1.0.1
Oisín Ryan
SEset: Computing Statistically-Equivalent Path Models
Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix. This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools. The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone. The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi:10.31234/osf.io/ryg69>.
Authors:
SEset_1.0.1.tar.gz
SEset_1.0.1.zip(r-4.5)SEset_1.0.1.zip(r-4.4)SEset_1.0.1.zip(r-4.3)
SEset_1.0.1.tgz(r-4.4-any)SEset_1.0.1.tgz(r-4.3-any)
SEset_1.0.1.tar.gz(r-4.5-noble)SEset_1.0.1.tar.gz(r-4.4-noble)
SEset_1.0.1.tgz(r-4.4-emscripten)SEset_1.0.1.tgz(r-4.3-emscripten)
SEset.pdf |SEset.html✨
SEset/json (API)
| # Install 'SEset' in R: |
| install.packages('SEset', repos = c('https://ryanoisin.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ryanoisin/seset/issues
- riskcor - Cognitive risk sample correlation matrix
Last updated 2 years agofrom:3d93eb9c32. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 10 2024 |
| R-4.5-win | NOTE | Sep 10 2024 |
| R-4.5-linux | NOTE | Sep 10 2024 |
| R-4.4-win | NOTE | Sep 10 2024 |
| R-4.4-mac | NOTE | Sep 10 2024 |
| R-4.3-win | NOTE | Sep 10 2024 |
| R-4.3-mac | NOTE | Sep 10 2024 |
Exports:cov_to_pathfind_parentsget_psinetwork_to_pathnetwork_to_SEsetorder_genpath_to_networkpropcalr2_distributionreorder_matSEset_to_network
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Path model from covariance matrix with ordering | cov_to_path |
| Return parent indices from a (weighted) DAG for a given child | find_parents |
| Calculate residual-covariance matrix based on a path model and covariance matrix | get_psi |
| Path model from ordered precision matrix | network_to_path |
| SE-set from precision matrix | network_to_SEset |
| Generate all topological orderings | order_gen |
| Precision matrix from ordered path model | path_to_network |
| Edge frequency in the SE-set | propcal |
| Compute Controllability Distribution in the SE-set | r2_distribution |
| Re-order rows and columns | reorder_mat |
| Cognitive risk sample correlation matrix | riskcor |
| Precision matrices from the SEset | SEset_to_network |