# spflow [](https://CRAN.R-project.org/package=spflow) [](https://github.com/LukeCe/spflow/actions) [](https://github.com/LukeCe/spflow/actions) [](https://CRAN.R-project.org/package=spflow) [](https://www.r-pkg.org/pkg/spflow) The [**spflow**](https://lukece.github.io/spflow/) package allows to estimate spatial econometric interaction models. It is designed to exploit the relational structure of flow data, reducing the computational burden and memory requirements. ## Installation You can install the released version of spflow from [CRAN](https://CRAN.R-project.org/package=spflow) with: ``` r install.packages("spflow") ``` And the development version from [GitHub](https://github.com/LukeCe/spflow/) with: ``` r # install.packages("devtools") devtools::install_github("LukeCe/spflow") ``` ## Example The package provides a new `spflow_network_multi-class` that combines information on the origins, the destinations, and the origin-destination pairs. Once this object is created, we can estimate an interaction model with the `spflow()` function. By default the model includes three autoregression parameters: rho_o, rho_d, rho_w. These parameters are related to origin-, destination-, and origin-to-destination-dependence. Through the formula interface we specify which variables are used as origin `O_()`, destination `D_()`, intra-regional `I_()` and OD-pair `P_()` characteristics. For more detailed examples have a look at the [package vignette](https://lukece.github.io/spflow/articles/paris_commute_flows.html). ``` r library("spflow") data("multi_net_usa_ge") spflow(y9 ~ O_(X) + D_(X) + I_(X) + P_(DISTANCE), multi_net_usa_ge) #> -------------------------------------------------- #> Spatial interaction model estimated by: MLE #> Spatial correlation structure: SDM (model_9) #> Dependent variable: y9 #> #> -------------------------------------------------- #> Coefficients: #> est sd t.stat p.val #> rho_d 0.497 0.030 16.499 0 #> rho_o 0.333 0.037 9.001 0 #> rho_w -0.227 0.044 -5.117 0 #> (Intercept) 10.198 2.161 4.719 0 #> (Intra) 9.871 1.531 6.445 0 #> D_X 0.983 0.069 14.321 0 #> D_X.lag1 0.509 0.115 4.437 0 #> O_X -0.759 0.038 -19.917 0 #> O_X.lag1 -0.367 0.093 -3.965 0 #> I_X 2.035 0.083 24.650 0 #> P_DISTANCE -2.622 0.384 -6.829 0 #> #> -------------------------------------------------- #> R2_corr: 0.9921423 #> Observations: 256 #> Model coherence: Validated ``` ## License [GPL 3](https://www.r-project.org/Licenses/GPL-3)