The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

slxr

Documentation: https://cwimpy.github.io/slxr/

Spatial-X (SLX) models for applied researchers.

slxr makes it easy to fit, interpret, and visualize Spatial-X regression models in R. Unlike existing tools that treat SLX as a consolation prize for SAR, slxr centers the SLX approach and provides first-class support for the features applied researchers actually need:

• Formula-based interface — write slx(y ~ x1 + x2, data, W, lag = "x1") and get a fitted model, not a wrestling match with listw objects.
• Variable-specific weights matrices — the defining feature of Wimpy, Whitten, and Williams (2021). Different covariates can spill over through different W matrices (contiguity, alliance, trade, etc.) in a single model.
• Higher-order spatial lags (W, W², W³) with clean effects decomposition.
• Temporally-lagged spatial variables (TSLS) for panel data.
• Tidy direct, indirect, and total effects — for SLX these don’t require matrix inversion or simulation.
• modelsummary-compatible output (via tidy() and glance() methods).
• Sensible defaults plus diagnostics for comparing W specifications.

Installation

# Development version
# install.packages("remotes")
remotes::install_github("cwimpy/slxr")

Example

library(slxr)
data(defense_burden)   # 1995 cross-section from Wimpy et al. (2021)

W_contig   <- slx_weights(style = "custom", matrix = defense_burden$W_contig,
                          row_standardize = FALSE)
W_alliance <- slx_weights(style = "custom", matrix = defense_burden$W_alliance,
                          row_standardize = FALSE)
W_defense  <- slx_weights(style = "custom", matrix = defense_burden$W_defense,
                          row_standardize = FALSE)

fit <- slx(
  ch_milex ~ milex_tm1 + log_pop_tm1 + civilwar_tm1 + total_wars_tm1 +
             alliance_us + ch_milex_us + ch_milex_ussr,
  data = defense_burden$data,
  spatial = list(
    civilwar_tm1   = W_contig,
    total_wars_tm1 = list(contig = W_contig, alliance = W_alliance),
    milex_tm1      = list(contig = W_contig, defense  = W_defense)
  )
)

slx_effects(fit)
slx_plot_effects(fit, types = c("indirect", "total"))

Variable-specific weights matrices:

fit <- slx(defense ~ civil_war + interstate_war + defense_lag,
           data = df,
           spatial = list(
             civil_war      = W_contig,
             interstate_war = W_contig,
             defense_lag    = list(W_contig, W_pact)
           ))

Status

Early development. The MVP covers single-W SLX estimation, effects decomposition, and modelsummary integration. Multi-W, higher-order, temporal, and plotting features are on the roadmap.

Citation

If you use slxr in published work, please cite both the package and the methodological paper it implements. Run citation("slxr") in R to see the current BibTeX entry, or refer to:

• Wimpy, C., Whitten, G. D., & Williams, L. K. (2021). X Marks the Spot: Unlocking the Treasure of Spatial-X Models. Journal of Politics, 83(2), 722–739. doi:10.1086/710089
• Wimpy, C. (2026). slxr: Spatial-X (SLX) Models for Applied Researchers. R package version 0.1.0. https://cwimpy.github.io/slxr/

References

Wimpy, C., Whitten, G. D., & Williams, L. K. (2021). X Marks the Spot: Unlocking the Treasure of Spatial-X Models. Journal of Politics, 83(2), 722–739. doi:10.1086/710089

Vega, S. H., & Elhorst, J. P. (2015). The SLX Model. Journal of Regional Science, 55(3), 339–363.

LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. Chapman & Hall/CRC.

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.
Health stats visible at Monitor.