Package: rbiogeme 0.1.2

Michel Bierlaire

rbiogeme: Interface to 'Biogeme'

Uses the 'Python' implementation of 'Biogeme' as the numerical backend for specifying and estimating discrete-choice models in R. The default native requirement is 'biogeme==3.3.5'.

Authors:Michel Bierlaire [aut, cre]

rbiogeme_0.1.2.tar.gz
rbiogeme_0.1.2.zip(r-4.7-any)rbiogeme_0.1.2.zip(r-4.6-any)
rbiogeme_0.1.2.tar.gz(r-4.7-any)rbiogeme_0.1.2.tar.gz(r-4.6-any)
rbiogeme_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rbiogeme/json (API)

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

Bug tracker:https://github.com/michelbierlaire/rbiogeme/issues

On CRAN:

Conda:

3.18 score 133 exports 12 dependencies

Last updated from:5bfc00a0fc. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK149
source / vignettesOK211
linux-releaseOK142
windows-develOK123
windows-releaseOK97
wasm-releaseOK122

Exports:assisted_specificationbayesian_estimatebayesian_posterior_mean_by_observationbayesian_stored_variablesbiogeme_betabiogeme_catalogbiogeme_catalog_controllerbiogeme_checkbiogeme_confidence_intervalsbiogeme_configbiogeme_controlbiogeme_databasebiogeme_database_columnsbiogeme_database_define_variablebiogeme_database_extract_rowsbiogeme_database_filtered_row_countbiogeme_database_has_columnbiogeme_database_is_panelbiogeme_database_materializebiogeme_database_nrowbiogeme_database_panelbiogeme_database_removebiogeme_database_row_idsbiogeme_database_segmentationbiogeme_diagnosticsbiogeme_drawsbiogeme_error_detailsbiogeme_general_statisticsbiogeme_generic_alt_specific_catalogsbiogeme_maxbiogeme_mdcev_modelbiogeme_minbiogeme_modelbiogeme_model_parametersbiogeme_native_parameter_namesbiogeme_panel_databasebiogeme_priorbiogeme_pythonbiogeme_sampling_partitionbiogeme_segmentationbiogeme_segmentation_catalogsbiogeme_setupboxcoxcatalogcatalog_configuration_idscatalog_controllercheck_derivativescheck_monte_carlo_stabilitycount_number_of_specificationscross_nested_log_probabilitycross_nested_logit_correlationcross_nested_logit_modelcross_nested_nestcross_nested_nestscross_nested_probabilitycross_nested_sparsity_reportcross_variabledatabase_define_variabledatabase_extract_rowsdatabase_generate_segmentationdatabase_removedefine_variablederiveDerivedistributed_parameterdrawelemElemestimateestimate_catalogestimate_configurationestimate_or_loadestimate_sampled_alternativesevaluate_biogeme_expressionevaluate_biogeme_expression_cgeneric_alt_specific_catalogsintegrate_normallinear_termlinear_utilityLinearTermTupleLinearUtilitylogitlogit_log_probabilitylogit_modellogit_probabilitylogzeromdcev_estimatemdcev_forecastmdcev_forecast_describemdcev_generate_epsilonsmdcev_modelmdcev_parameter_tablemdcev_short_summarymdcev_validate_forecastmonte_carlonested_endogenous_sampling_log_probabilitynested_log_probabilitynested_logit_correlationnested_logit_modelnested_nestnested_nestsnested_probabilitynormal_cdfnormal_pdfordered_logit_log_probabilityordered_probit_log_probabilitypanel_likelihood_trajectorypareto_post_processingpiecewiseprepare_swissmetroprofile_jaxquick_estimaterandom_variableread_resultsremove_observationsrun_assisted_specificationsafe_expsafe_logsampled_alternatives_modelsampling_segment_sizessave_resultssegment_betasegmentation_catalogssegmented_betasimulatesimulate_bayesiansimulate_single_formulaswissmetro_b01b_modelswissmetro_dataswissmetro_mnl_modelvalidatevalidate_modelvariable

Dependencies:herejsonlitelatticeMatrixpngrappdirsRcppRcppTOMLreticulaterlangrprojrootwithr

Advanced models with rbiogeme
Monte Carlo integration and named draws | Bayesian estimation | Piecewise, Box--Cox, and symbolic derivatives | MDCEV estimation and forecasting | Catalogs, segmentation, and assisted specification | Sampling of alternatives | Hybrid-choice specifications | Equivalence and reproducibility checklist
Rendered fromadvanced-models.Rmdusingknitr::rmarkdown

Last update: 2026-09-29
Started: 2026-09-29

Getting started with rbiogeme
Installation and Python configuration | Data and expressions | A complete multinomial logit model | Choosing an output directory | Generic likelihoods and simulations | Database operations | Reproducible output | Finding the right starting point
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-29
Started: 2026-09-29

Modeling workflows with rbiogeme
A reusable example database | Multinomial logit with availability, codes, and weights | Nested and cross-nested logit | Panel likelihoods | Simulation and diagnostics | Fresh estimation and explicit result loading | Controlling native behavior
Rendered frommodeling-workflows.Rmdusingknitr::rmarkdown

Last update: 2026-09-29
Started: 2026-09-29

Readme and manuals

Help Manual

Help pageTopics
Interface to Biogemerbiogeme-package
Absolute value of a Biogeme expressionabs.biogeme_expression
Run native Biogeme assisted specificationassisted_specification run_assisted_specification
Estimate a model with native Bayesian inferencebayesian_estimate
Retrieve native posterior means by observationbayesian_posterior_mean_by_observation
Report variables stored in native Bayesian resultsbayesian_stored_variables
Create a Biogeme parameterbiogeme_beta
Create a native-compatible expression catalogbiogeme_catalog catalog
Create a neutral controller for one or more expression catalogsbiogeme_catalog_controller catalog_controller
Check whether rbiogeme is ready to run a modelbiogeme_check
Calculate native simulation confidence intervalsbiogeme_confidence_intervals
Configure the Python runtime used by rbiogemebiogeme_config
Define estimation and simulation controlsbiogeme_control
Create a Biogeme databasebiogeme_database
Return the columns available in a Biogeme databasebiogeme_database_columns
Define a derived database variable using a complete Biogeme expressionbiogeme_database_define_variable database_define_variable define_variable
Extract observations from a Biogeme databasebiogeme_database_extract_rows database_extract_rows
Return the number of observations excluded by native database filtersbiogeme_database_filtered_row_count
Test whether a Biogeme database contains a columnbiogeme_database_has_column
Return whether a database has a declared panel structurebiogeme_database_is_panel
Materialize lazy database operations through native Biogemebiogeme_database_materialize
Return the number of rows currently represented by a databasebiogeme_database_nrow
Declare and validate a panel identifierbiogeme_database_panel
Remove observations satisfying a Biogeme logical expressionbiogeme_database_remove database_remove remove_observations
Return the original row identifiers of a Biogeme databasebiogeme_database_row_ids
Generate a native-compatible segmentation specification from a databasebiogeme_database_segmentation database_generate_segmentation
Report the active R, Python, Biogeme, and numerical-library versionsbiogeme_diagnostics
Declare first-class draw metadata for a modelbiogeme_draws
Return the diagnostic details attached to a Biogeme errorbiogeme_error_details
Return native Biogeme general estimation statisticsbiogeme_general_statistics
Build synchronized generic/alternative-specific catalogsbiogeme_generic_alt_specific_catalogs generic_alt_specific_catalogs
Maximum of Biogeme expressionsbiogeme_max
Construct an MDCEV model specificationbiogeme_mdcev_model mdcev_model
Minimum of Biogeme expressionsbiogeme_min
Create a generic Biogeme modelbiogeme_model
Return the parameter definitions in a modelbiogeme_model_parameters
Return parameter names collected from the compiled native expressionbiogeme_native_parameter_names
Construct a panel database in one callbiogeme_panel_database
Declare a native Bayesian priorbiogeme_prior
Initialize and return the Python interpreter used by rbiogemebiogeme_python
Define a native Biogeme sampling partitionbiogeme_sampling_partition
Define a discrete parameter segmentationbiogeme_segmentation
Build synchronized catalogs for possible parameter segmentationsbiogeme_segmentation_catalogs segmentation_catalogs
Automatically prepare the native Biogeme runtimebiogeme_setup
Box-Cox transformationboxcox
Return catalog configuration identifiers using native Biogemecatalog_configuration_ids
Check analytical derivatives against native finite differencescheck_derivatives
Run native post-estimation Monte Carlo draw-stability diagnosticscheck_monte_carlo_stability
Count catalog specifications using native Biogemecount_number_of_specifications
Construct a native cross-nested-logit log-probability expressioncross_nested_log_probability
Calculate the native cross-nested-logit error-term correlation matrixcross_nested_logit_correlation
Define a cross-sectional cross-nested-logit modelcross_nested_logit_model
Define one cross-nested-logit nestcross_nested_nest
Define the complete cross-nested-logit nest structurecross_nested_nests
Construct a native cross-nested-logit probability expressioncross_nested_probability
Summarize the structural sparsity of a CNL nest specificationcross_nested_sparsity_report
Define a sampled-alternative cross-variablecross_variable
Symbolically differentiate an expression with respect to a named variableDerive derive
Store a simulated individual-level parameter in Bayesian resultsdistributed_parameter
Create a named Biogeme draw nodedraw
Select an expression from a numeric mappingElem elem
Estimate a Biogeme modelestimate
Estimate every specification in a native Biogeme catalogestimate_catalog
Estimate one named native catalog configurationestimate_configuration
Estimate or explicitly load a standard Biogeme resultestimate_or_load
Estimate a native sampled-alternative modelestimate_sampled_alternatives
Evaluate a standalone expression with native Biogemeevaluate_biogeme_expression
Evaluate one expression with native row-wise or aggregated JAX calculationevaluate_biogeme_expression_c
Integrate an expression over a standard normal random variableintegrate_normal
Define one coefficient-variable term for a linear utilityLinearTermTuple linear_term
Construct a native linear utility expressionLinearUtility linear_utility
Construct a native logit log-probability expressionlogit_log_probability
Define a cross-sectional multinomial logit modellogit_model
Construct a native logit probability expressionlogit logit_probability
A numerically safe logarithm that returns zero at zerologzero safe_log
Estimate an MDCEV model with native Biogememdcev_estimate
Forecast MDCEV consumption using native Biogeme algorithmsmdcev_forecast
Return native pandas description tables for an MDCEV forecastmdcev_forecast_describe
Generate native MDCEV error-term drawsmdcev_generate_epsilons
Return native Biogeme's estimated-parameter table for an MDCEV fitmdcev_parameter_table
Return native Biogeme's compact MDCEV estimation summarymdcev_short_summary
Validate the two native MDCEV forecasting algorithmsmdcev_validate_forecast
Monte Carlo average of an expressionmonte_carlo
Construct a native nested-logit log probability with endogenous-sampling correctionnested_endogenous_sampling_log_probability
Construct a native nested-logit log-probability expressionnested_log_probability
Calculate the native nested-logit error-term correlation matrixnested_logit_correlation
Define a cross-sectional nested-logit modelnested_logit_model
Define one non-trivial nested-logit nestnested_nest
Define the complete nested-logit nest structurenested_nests
Construct a native nested-logit probability expressionnested_probability
Normal cumulative distribution functionnormal_cdf
Normal probability density functionnormal_pdf
Construct an ordered-logit log-likelihood expressionordered_logit_log_probability
Construct an ordered-probit log-likelihood expressionordered_probit_log_probability
Aggregate an observation-level likelihood over a panel trajectorypanel_likelihood_trajectory
Re-estimate the Pareto-optimal models saved by native Biogemepareto_post_processing
Piecewise-linear expression using native Biogeme naming rulespiecewise
Predict native probabilities or simulation expressionspredict.biogeme_fit
Profile native JAX formula evaluationprofile_jax
Estimate a Biogeme model using the native quick-estimation operationquick_estimate
Create a random variable for native numerical integrationrandom_variable
Read standard Biogeme YAML resultsread_results
Numerically safe exponentialsafe_exp
Define a sampled-alternative Biogeme modelsampled_alternatives_model
Generate balanced sampling segment sizessampling_segment_sizes
Save standard Biogeme YAML resultssave_results
Create a segmented parameter expressionsegmented_beta segment_beta
Simulate named native Biogeme expressions at fixed estimatessimulate
Simulate formulas over Bayesian posterior drawssimulate_bayesian
Evaluate one native Biogeme formula as an aggregated scalarsimulate_single_formula
Square root of a Biogeme expressionsqrt.biogeme_expression
Build the segmented linear-utility Swissmetro specification from b01bswissmetro_b01b_model
Prepare the Swissmetro database used by Biogeme's examplesprepare_swissmetro swissmetro_data
Build the baseline Swissmetro MNL specificationswissmetro_mnl_model
Validate a model using native Biogeme cross-validationvalidate
Validate a model before estimationvalidate_model
Create a Biogeme data variablevariable