README.md

In palsr: Projected Actor Locations for Spatial Interaction Modeling

palsr

palsr implements the Projected Actor Location (PALS) method for spatial modeling of dyadic interactions between geographically mobile actors, as described in Kim, Liu and Desmarais (2023), Spatial modeling of dyadic geopolitical interactions between moving actors, Political Science Research and Methods 11(3).

Actors such as armed groups, firms, or diplomats have no fixed location: they move through space over time, and where two such actors interact is itself an outcome worth modeling. PALS projects where a mobile actor "is" at any moment from the spatiotemporal history of its past interactions, using exponential-smoothing weights that favour recent events, and combines a focal actor's own history with that of its interaction partners ("alters").

Installation

install.packages("remotes")   # if not already installed
remotes::install_github("bdesmarais/palsr")

The package compiles a small amount of C++ via Rcpp, so a working toolchain (Rtools on Windows, Xcode command-line tools on macOS) is required.

The method

For a focal actor i at time t, the projected location is a convex combination of a recency-weighted mean of i's own past event locations and a recency-weighted mean of its alters' event locations:

g_i(t) = (1 - π) Σ W_i(e) g(e)  +  π Σ W_k(e) g(e),   π = logistic(γ + η·v)

with four smoothing parameters:

| Parameter | Role | |-----------|-----------------------------------------------------| | alpha | time-decay of the focal actor's own history | | beta | time-decay of the alters' histories | | gamma | intercept of the focal-vs-alter mixing weight π | | eta | dependence of π on relative event activity v |

A reduced one-parameter model fixes π = 0 (focal history only) and estimates alpha alone.

Quick start

library(palsr)

# Bundled simulated dataset: 1,500 dyadic events, 25 mobile actors, 2000-2016.
data(nigeria_sim)

# Estimate the one-parameter model (marching forward in time, minimizing
# great-circle prediction error).
fit <- estimate_pals(nigeria_sim, model = "one")
fit
#> <pals_fit> one-parameter PALS model
#> <pals_params> (one-parameter model)
#>   alpha = 0.874  (pi fixed at 0)
#>   objective (mean Haversine km): 103.37 over 1500 events

# Project where each actor is on a given date.
project_pals(nigeria_sim, predict_time = as.Date("2015-01-01"), params = fit)

# Predict interaction locations and score them in kilometres.
predict_event_locations(nigeria_sim, nigeria_sim, fit)

# Build the dyadic distance covariate (optionally log-transformed).
dyads <- data.frame(actor1 = "G01", actor2 = "G02", time = as.Date("2014-06-01"))
pal_distance(nigeria_sim, dyads, fit, transform = "log")

The full four-parameter model adds the alter component:

fit4 <- estimate_pals(nigeria_sim, model = "four")
coef(fit4)

Uncertainty

bootstrap_pals() resamples events with replacement and re-estimates the model on each replicate, giving bootstrap standard errors and percentile intervals. Downstream estimands computed per replicate can be pooled with Rubin's Rules via pool_rubin():

bt <- bootstrap_pals(nigeria_sim, R = 10, model = "one", seed = 1)
summary(bt)

# Pool five per-replicate estimates (q) and their variances (u).
pool_rubin(q = c(1.10, 0.95, 1.20, 1.05, 0.98),
           u = c(0.04, 0.05, 0.045, 0.038, 0.052))

Learn more

See the vignette for a full worked example:

vignette("palsr")

Key functions

| Function | Purpose | |------------------------------|----------------------------------------------------| | pal_events() | Build a validated dyadic-event table | | simulate_conflict_events() | Simulate mobile-actor interaction data | | estimate_pals() | Estimate parameters by Haversine error minimization| | project_pal() / project_pals() | Project actor locations | | predict_event_locations() | Predict dyadic interaction locations | | pal_distance() | Dyadic distance covariate between projected locations | | bootstrap_pals() | Nonparametric bootstrap uncertainty | | pool_rubin() | Pool replicate estimates with Rubin's Rules |

Citation

If you use palsr, please cite the method paper:

Kim, S., Liu, H., & Desmarais, B. A. (2023). Spatial modeling of dyadic geopolitical interactions between moving actors. Political Science Research and Methods, 11(3), 617–635. https://doi.org/10.1017/psrm.2022.6

Contributing and support

Bug reports, questions, and contributions are welcome. Please see CONTRIBUTING.md for guidelines on reporting issues, contributing code, and seeking support, and use the issue tracker to get in touch.

License

MIT © the package authors.

palsr documentation built on July 1, 2026, 5:07 p.m.