Package: causalSLSE 0.4-1

Pierre Chausse Developer

causalSLSE: Semiparametric Least Squares Inference for Causal Effects

Several causal effects are measured using least squares regressions and basis function approximations. Backward and forward selection methods based on different criteria are used to select the basis functions.

Authors:Pierre Chausse Developer [aut, cre], Mihai Giurcanu Developer [aut]

causalSLSE_0.4-1.tar.gz
causalSLSE_0.4-1.tar.gz(r-4.7-arm64)causalSLSE_0.4-1.tar.gz(r-4.7-x86_64)causalSLSE_0.4-1.tar.gz(r-4.6-arm64)causalSLSE_0.4-1.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
causalSLSE/json (API)

# Install 'causalSLSE' in R:
install.packages('causalSLSE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • nsw - Lalonde Subsample of the National Supported Work Demonstration Data
  • simDat1 - Simulated Data
  • simDat2 - Simulated Data
  • simDat3 - Simulated Data
  • simDat4 - Simulated Data.
  • simDat5 - Simulated Data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblasfortran

2.00 score 2 scripts 198 downloads 34 exports 12 dependencies

Last updated from:5d7e1c0e36. Checks:4 WARNING, 1 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING126
linux-devel-x86_64WARNING140
source / vignettesOK190
linux-release-arm64WARNING133
linux-release-x86_64WARNING130
wasm-releaseFAIL158

Exports:as.modelcausalSLSEcausalSLSE.cslseFitcausalSLSE.cslseModelcausalSLSE.formulacslseModelestSLSEestSLSE.cslseModelextractipwLLmatchingllSplinesllSplines.cslseModelllSplines.slseModelmatchingplot.cslseFitplot.slseFitpredict.cslseFitpredict.slseFitprint.cslseprint.cslseFitprint.cslseModelprint.slseFitprint.slseKnotsprint.summary.cslseprint.summary.cslseFitprint.summary.slseFitselSLSEselSLSE.cslseModelslseKnotsslseModelsummary.cslsesummary.cslseFitsummary.slseFit

Dependencies:askpasscurlhttrjsonlitelatticemimeopensslR6sandwichsystexregzoo

Semiparametric Least Squares Inference for Causal Effects with R

Pierre Chausse^[University of Waterloo, [email protected]], Mihai Giurcanu^[University of Chicago, [email protected]], Marinela Capanu^[Memorial Sloan Kettering Cancer Center, [email protected]], George Luta^[Georgetown University, [email protected]]

Rendered fromcausalSLSE.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2025-08-26
Started: 2024-01-18

Readme and manuals

Help Manual

Help pageTopics
Alternative Causal Effect estimation methods.altCausal ipw LLmatching matching
Converter into Model Objectsas.model as.model.cslse as.model.cslseFit as.model.slseFit
Causal Effect Based on Semiparametric Least Squares ModelscausalSLSE causalSLSE.cslseFit causalSLSE.cslseModel causalSLSE.formula
Semiparametric Least Squares Estimator ModelcslseModel slseModel
Least Squares Estimate of 'cslseModel' or 'slseModel' ObjectsestSLSE estSLSE.cslseModel estSLSE.slseModel
'extract' methods for some objects.extract,altCausal-method extract,cslse-method extract,slseFit-method extract.cslse extract.slseFit
Local Linear Splines Generator for Model ObjectsllSplines llSplines.cslseModel llSplines.slseModel
Lalonde Subsample of the National Supported Work Demonstration Data (NSW)nsw
Plot of Predicted Outcomeplot.cslseFit plot.slseFit
Outcome Predictionpredict.cslseFit predict.slseFit
Print Methodsprint.cslse print.cslseFit print.cslseModel print.slseFit print.slseKnots print.slseModel print.summary.cslse print.summary.cslseFit print.summary.slseFit
Knots Selection MethodselSLSE selSLSE.cslseModel selSLSE.slseModel
Simulated DatasimDat1
Simulated DatasimDat2
Simulated DatasimDat3
Simulated Data.simDat4
Simulated DatasimDat5
Knots Creator for Basis FunctionsslseKnots
Summary Method for Fitted Modelssummary.cslse summary.cslseFit summary.slseFit
Update Methodsupdate.cslseModel update.slseKnots update.slseModel