defm: Estimation and Simulation of Multi-Binary Response Models

Multi-binary response models are a class of models that allow for the estimation of multiple binary outcomes simultaneously. This package provides functions to estimate and simulate these models using the Discrete Exponential-Family Models [DEFM] framework. In it, we implement the models described in Vega Yon, Valente, and Pugh (2023) <doi:10.48550/arXiv.2211.00627>. DEFMs include Exponential-Family Random Graph Models [ERGMs], which characterize graphs using sufficient statistics, which is also the core of DEFMs. Using sufficient statistics, we can describe the data through meaningful motifs, for example, transitions between different states, joint distribution of the outcomes, etc.

Version: 0.1-1
Depends: R (≥ 2.10), stats4
Imports: Rcpp, stats
LinkingTo: Rcpp
Suggests: texreg
Published: 2023-09-07
Author: George Vega Yon ORCID iD [aut, cre], Department of Veterans Affairs - Rehabilitation, Research, and Development Service [fnd] (Award/W81XWH-18-PH/TBIRP-LIMBIC under Award No. I01 RX003443)
Maintainer: George Vega Yon <g.vegayon at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: defm citation info
Materials: README NEWS
CRAN checks: defm results


Reference manual: defm.pdf


Package source: defm_0.1-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): defm_0.1-1.tgz, r-oldrel (arm64): defm_0.1-1.tgz, r-release (x86_64): defm_0.1-1.tgz, r-oldrel (x86_64): defm_0.1-1.tgz
Old sources: defm archive


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