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netmatchRI provides network-constrained matching and
randomization-based inference for observational network data.
Dual-penalty matching minimizes total covariate dissimilarity while
enforcing penalties on network proximity between and within treatment
arms in matched sets. Randomization-based inference combines evidence
from correlated matched sets based on this design.
Install the source archive supplied with the release:
install.packages("netmatchRI_0.1.0.tar.gz", repos = NULL, type = "source")
library(netmatchRI)HiGHS is the default open-source solver. Gurobi is optional and requires a separate optimizer, license, and R package; see the official installation guide.
The package includes simulated data and a network. The guide covers matching, diagnostics, randomization-based inference, and sensitivity analysis:
data("netmatch_example", package = "netmatchRI")
vignette("netmatchRI", package = "netmatchRI")The same workflow is available as a runnable script in inst/examples/basic-workflow.R.
After installation, locate it with:
system.file("examples", "basic-workflow.R", package = "netmatchRI")Use citation("netmatchRI") for the installed citation.
To report a problem or request a feature, open an issue at https://github.com/zhejiadong/netmatchRI/issues.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.