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