FPScausal: Functional Propensity Score for Causal Inference

Implements functional propensity score (FPS) weighting for causal inference with functional treatments. Weights are estimated by maximising the empirical likelihood subject to covariate-balancing constraints and solving the resulting dual problem via the BFGS quasi-Newton algorithm, following Ciardulli, S. and Fontana, N. (2026). The package supports scalar, binary, and functional outcomes, as well as functional covariates.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	fda (≥ 6.0.0), ggplot2 (≥ 3.4.0), tidyr (≥ 1.2.0), MASS (≥ 7.3-0), wCorr, patchwork (≥ 1.1.0), progress (≥ 1.2.0), stats, utils
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-08-09
DOI:	10.32614/CRAN.package.FPScausal (may not be active yet)
Author:	Nicole Fontana [aut, cre], Simone Ciardulli [aut]
Maintainer:	Nicole Fontana <nicole.fontana at polimi.it>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
CRAN checks:	FPScausal results

Documentation:

Reference manual:	FPScausal.html , FPScausal.pdf
Vignettes:	FPScausal: Functional propensity score weighting for causal inference with functional treatments, covariates, and outcomes (source, R code)

Downloads:

Package source:	FPScausal_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): FPScausal_0.1.0.tgz, r-oldrel (arm64): FPScausal_0.1.0.tgz, r-release (x86_64): FPScausal_0.1.0.tgz, r-oldrel (x86_64): FPScausal_0.1.0.tgz

Linking:

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