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choicer 0.1.0

Initial CRAN release.

Supported models

• Multinomial Logit (run_mnlogit()) — estimation, prediction, elasticities, diversion ratios, BLP contraction
• Mixed Logit (run_mxlogit()) — normal and log-normal random coefficients, correlated random coefficients via Cholesky, Halton draws, elasticities, BLP contraction
• Nested Logit (run_nestlogit()) — estimation with nest-specific dissimilarity (lambda) parameters

S3 class system

• Parent class choicer_fit with subclasses choicer_mnl, choicer_mxl, choicer_nl
• Standard methods: summary(), coef(), vcov(), logLik(), AIC(), BIC(), nobs(), predict()
• Classed data objects: choicer_data_mnl, choicer_data_mxl, choicer_data_nl from prepare_*_data()

Post-estimation generics

• elasticities() — methods for MNL and MXL
• diversion_ratios() — method for MNL
• blp() — BLP contraction for MNL and MXL

API

• Dual workflow for all run_*logit() functions: convenience (pass data + column names) or advanced (pass pre-prepared input_data)
• Pluggable optimizer via optimizer = "nloptr" | "optim" | <function>
• prepare_nl_data() added for nested logit data preparation

Computation

• C++ likelihoods, gradients, and analytical Hessians via Rcpp/RcppArmadillo
• OpenMP parallelization over individuals
• Log-sum-exp trick throughout for numerical stability

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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