Changes in version 0.1.0.9000                      

Post-estimation

  - wtp() — willingness-to-pay for MNL, MXL, and NL with analytic
    delta-method standard errors. For MXL, log-normal random
    coefficients report the median WTP under the package's shifted
    log-normal parameterization; random price coefficients are rejected
  - gof() — goodness of fit: McFadden pseudo R-squared (plain and
    adjusted) and in-sample hit rate, with "equal_shares" (default) and
    "market_shares" null models; now also shown in the summary() footer
  - predict(..., newdata = ) — counterfactual prediction for all three
    models, from either a long data.frame in the fit-time format or a
    modified-design list (X, alt_idx, M, ...); works even with keep_data
    = FALSE. NL fits now store nest_idx top-level to support this
  - logsum() — expected maximum utility (inclusive value) per choice
    situation for MNL (closed form), MXL (simulated with a dedicated
    per-draw kernel, avoiding the Jensen bias of averaging utilities
    first), and NL (nested inclusive-value formula)
  - consumer_surplus() — expected consumer surplus logsum / (-alpha)
    (Train 2009, Ch. 3) with a delta-method standard error of the mean
    CS for MNL; supports newdata for policy ΔCS analysis

                 Changes in version 0.1.0 (2026-05-20)                  

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