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spldv  

Spatial Models for Limited Dependent Variables
View on CRAN: Click here


Download and install spldv package within the R console
Install from CRAN:
install.packages("spldv")

Install from Github:
library("remotes")
install_github("cran/spldv")

Install by package version:
library("remotes")
install_version("spldv", "0.1.3")



Attach the package and use:
library("spldv")
Maintained by
Mauricio Sarrias
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-03-22
Latest Update: 2023-08-10
Description:
The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.
How to cite:
Mauricio Sarrias (2022). spldv: Spatial Models for Limited Dependent Variables. R package version 0.1.3, https://cran.r-project.org/web/packages/spldv. Accessed 12 Mar. 2025.
Previous versions and publish date:
0.1.0 (2022-03-22 18:40), 0.1.1 (2022-05-09 21:50), 0.1.2 (2023-09-22 14:00)
Other packages that cited spldv R package
View spldv citation profile
Other R packages that spldv depends, imports, suggests or enhances
Complete documentation for spldv
Functions, R codes and Examples using the spldv R package
Some associated functions: getSummary.bingmm . getSummary.binlgmm . impacts.bingmm . impacts . sbinaryGMM . sbinaryLGMM . 
Some associated R codes: HiddenForLater.R . marginalEffects.R . sbinaryGMM.R . sbinaryLGMM.R .  Full spldv package functions and examples
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