Package: defm 0.3.0.0

George Vega Yon

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.

Authors:George Vega Yon [aut, cre], Department of Veterans Affairs - Rehabilitation, Research, and Development Service [fnd], U.S. Army Medical Research Acquisition Activity [fnd]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
defm/json (API)

# Install 'defm' in R:
install.packages('defm', repos = c('https://uofuepibio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uofuepibio/defm/issues

Pkgdown/docs site:https://uofuepibio.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

ergmmlemodelingsimulationstatisticscppopenmp

3.70 score 1 stars 4 scripts 43 downloads 32 exports 2 dependencies

Last updated from:a0f7148148. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK98
linux-devel-x86_64OK127
source / vignettesOK255
linux-release-arm64OK96
linux-release-x86_64OK110
macos-release-arm64OK119
macos-release-x86_64OK285
macos-oldrel-arm64OK109
macos-oldrel-x86_64OK275
windows-devel-arm64OK191
windows-devel-x86_64OK153
windows-release-arm64OK214
windows-release-x86_64OK103
windows-oldrel-x86_64OK125
wasm-releaseOK94

Exports:defm_mleget_countersget_statsget_X_namesget_Y_namesinit_defmloglike_defmloglike_defm_gradloglike_defm_hesslogoddsmorder_defmmotif_censusncol_defm_xncol_defm_ynew_defmnobs_defmnrow_defmnterms_defmprint_statsrule_constrain_supportrule_not_one_to_zeroset_counter_infoset_counters_namesshowsim_defmsummarysummary_tabletd_formulatd_generictd_logit_intercepttd_onestexreg_fancy

Dependencies:barryRcpp

Readme and manuals

Help Manual

Help pageTopics
Discrete Exponential Family Model (DEFM)DEFM defm init_defm morder_defm ncol_defm_x ncol_defm_y new_defm new_defm_cpp nobs_defm nrow_defm nterms_defm print_stats
Fitted DEFMdefm_mle-class show,defm_mle-method show,summary.defm_mle-method summary,defm_mle-method summary.defm_mle-class
Model specification for DEFM+.DEFM defm_terms rule_constrain_support rule_not_one_to_zero td_formula td_generic td_logit_intercept td_ones terms_defm
Access to the names of a model's datasetsdefm-names get_X_names get_Y_names
Extract the counters from a DEFM modelas.list.DEFM_counter as.list.DEFM_counters get_counters length.DEFM_counters set_counters_names set_counters_names.DEFM set_counters_names.DEFM_counters set_counter_info [.DEFM_counters
Get sufficient statistics countsget_stats
Log-Likelihood of DEFMloglike_defm
Gradient and Hessian of the DEFM log-likelihoodloglike_defm_grad loglike_defm_hess
Maximum Likelihood Estimation of DEFMdefm_mle logodds texreg_fancy
Motif censusdefm_motif_census motif_census
Simulate data using a DEFMsim_defm
Summary table of a fitted DEFMas.data.frame.defm_summary_table format.defm_summary_table print.defm_summary_table summary_table
Valente's SNS datavalentesns valentesnsList