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choicer 0.2.0

Public API cleanup (breaking)

• Low-level C++ likelihood, gradient, Hessian, score, prediction, post-estimation, and Gibbs-sampler wrappers are now internal implementation details rather than exported package functions. High-level fitting, preparation, S3 post-estimation, simulation, and recovery APIs are unchanged. The expert-facing raw BLP contractions, get_halton_normals(), set_num_threads(), and thread_info() remain public. With no downstream CRAN dependencies, v0.2.0 is the least disruptive point to narrow this surface before applications depend on unstable kernel signatures.

Robust and clustered standard errors (MNL / MXL / NL)

• vcov() on a fitted MNL, MXL, or NL model gains type = and cluster = arguments for post-hoc variance recomputation without refitting (requires keep_data = TRUE): "hessian" (inverse analytical Hessian), "bhhh" (OPG), "robust" (Huber-White / WESML sandwich), and "cluster" (cluster-robust sandwich over within-cluster sums of weighted scores — use it when the same decision maker contributes several choice situations). With no arguments, vcov() returns the as-fitted variance, unchanged.
• New cluster_col= argument on run_mnlogit() / run_mxlogit() / run_nestlogit() (and the corresponding prepare_*_data() functions): supplies per-situation cluster labels at fit time and selects the new se_method = "cluster".
• All score-based variances (BHHH, robust, cluster) are now assembled in R from one per-situation score matrix, computed by new internal C++ kernels that reuse the BHHH loop bodies. The existing se_method = "sandwich" / wesml_vcov() results are unchanged (the robust meat is the w^2 special case of the shared path).
• Scope note (MXL): clustering repairs the inference, not the estimand. The MXL simulated likelihood treats each choice situation as an independent draw from the mixing distribution (cross-sectional MSL, not the panel product form), so clustered standard errors on panel data are robust to within-person dependence but do not turn the fit into a panel mixed logit. For panel random coefficients use run_hmnlogit() (person_col=).

Corrections

• blp() for an MXL fit using draws = "generate" now carries the fitted draw count, seed, and digit-permutation setting through every contraction evaluation. Previously that path could evaluate counterfactual shares with fallback draw metadata rather than the simulation design used for the fit.
• On-the-fly MXL draws now accept all 128 prime-base dimensions implemented by the generator (the prior guard rejected the 128th dimension); 129 or more random coefficients still fail early with an explicit limit.
• HMNL prediction and welfare now use a taskwise max-shifted softmax/logsum, so extreme counterfactual utilities remain finite. Counterfactual compensating variation accepts explicit non-negative task weights and matches baseline and policy choice situations by (person_col, id_col) before subtraction; it now rejects added, dropped, or substituted tasks instead of pairing them silently by sorted position.
• The documentation previously claimed that MCMC draws from the Gibbs samplers (run_mnprobit(), run_hmnlogit(), run_hmnprobit()) are bitwise reproducible regardless of the OpenMP thread count. Independent validation disconfirmed this: draws are reproducible given the seed and a fixed thread count, and across different thread counts are invariant only up to floating-point reduction-order round-off (~1e-15), not bitwise. All man pages, vignettes, and NEWS entries now state the correct guarantee.
• Documentation now distinguishes the frequentist models’ analytical derivatives from the Bayesian C++ samplers, labels run_mxlogit() as a cross-sectional simulated likelihood, and no longer directs users to WTP-space estimation or bounded/censored mixing distributions as if choicer implemented them. The README also labels mode_choice shares and welfare as sample-design quantities unless external population shares and WESML weights are supplied.
• The MNP tutorial no longer recommends unavailable user-specified starts and now states its current post-estimation boundary. HMNP documentation now makes explicit that it uses iid utility-level normal shocks rather than the full differenced-error covariance estimated by run_mnprobit().

Hierarchical Bayes (HMNL/HMNP) convergence diagnostics and multi-chain performance

• New ess(draws) (rank-normalized bulk and tail effective sample size) and mcse(draws, kind = c("mean", "median")) (Monte Carlo standard error of the mean or median), following Vehtari, Gelman, Simpson, Carpenter & Bürkner (2021, Bayesian Analysis). rhat() gains a rank = TRUE option for the same paper’s rank-normalized, folded R-hat; the default rank = FALSE reproduces the original split R-hat exactly.
• New traceplot() generic, with a choicer_hb method, for visual inspection of the b/theta/sigma_d2 chains (and user-selected delta columns).
• summary() on a choicer_hmnl/choicer_hmnp fit now prints one consolidated multi-chain diagnostics table (R-hat, ESS bulk, ESS tail, and MCSE per parameter block, plus a single worst-case summary row spanning all J delta_j alternative effects) in place of the previous bare split-R-hat printout. HMNP now always prints an explicit “conjugate — no acceptance step” line where HMNL prints its beta/delta acceptance rates. The fit-time convergence warning now checks rank-R-hat and ESS bulk across every tracked parameter, including all J delta_j columns (previously only b/theta/sigma_d2 were checked).
• run_hmnlogit() / run_hmnprobit() fits now retain all requested chains’ hierarchical draws in a new object$chains field (a list, one element per chain: b, w_vech, delta, theta, sigma_d2, plus loglik for HMNL / sigma2 for HMNP), which the multi-chain diagnostics above consume. object$draws (chain 1) and object$beta_i (chain-1-only respondent taste summaries/draws) are unchanged.
• Chains requested via chains= are sampled sequentially, and all of their hierarchical draws are retained (object$chains) for the multi-chain diagnostics above. Parallel multi-chain execution is planned for a future release.
• The keep_beta_i = "draws" memory guard on both models is now based on a measured ~1.9x Rcpp::wrap()/R-list overhead factor (the previous formula under-estimated true memory use by roughly 2x) and accounts for all requested chains; it still fails fast before the chain runs rather than after an out-of-memory blow-up.

Hierarchical Bayesian MNL and MNP (BLP-style random-effects ASCs)

• New run_hmnlogit() (class choicer_hmnl) and run_hmnprobit() (class choicer_hmnp): hierarchical Bayes discrete choice with two decoupled random-effect levels — respondent-level structural tastes beta_i ~ N(b, W) (W is K x K, no ASC columns) and a global BLP-style alternative effect delta_j = z_j' theta + xi_j, xi_j ~ N(0, sigma_d^2) (one scalar variance regardless of J; partial pooling toward the characteristics-based mean; posterior-predictive delta for alternatives outside the estimation sample). Both models carry a first-class implicit outside option (systematic utility 0 plus its own shock) that anchors the location of delta — no base alternative or sum-to-zero constraint. Panel and cross-sectional (person_col = NULL, T_i = 1) modes share one code path.
• HMNL: adaptive RW-Metropolis-within-Gibbs on the mnprobit engine pattern (single persistent OpenMP region, per-(iteration, unit) RNG streams; draws are reproducible given the seed and a fixed thread count, and invariant across thread counts only up to floating-point reduction-order round-off, ~1e-15), with a strictly serial delta sweep (the conditionals are coupled through the softmax denominators) at O(1) incremental cost per affected task; supports log-normal coordinates via rc_dist. HMNP: fully conjugate Albert-Chib augmentation in un-differenced utility space with iid N(0, sigma^2) shocks, a non-identified sigma^2 chain (parameter expansion), and per-draw scale normalization of every identified quantity.
• Priors: b ~ N(b_bar, A^-1), W ~ IW(nu, V), theta ~ N(theta_bar, A_theta^-1), and sigma_d ~ half-Cauchy(0, s_d) via the Makalic-Schmidt conjugate scale mixture (IG fallback available).
• New preps prepare_hmnl_data() / prepare_hmnp_data() (two-level person/task indexing, implicit-outside convention shared with prepare_mnl_data(), alternative-level design Z, optional control-function residual column for price endogeneity per Petrin & Train 2010) and DGPs simulate_hmnl_data() / simulate_hmnp_data().
• Post-estimation on the shared choicer_hb class, all posterior-draw based: predict() (population/individual, entry counterfactuals via the posterior-predictive delta; HMNP probabilities by deterministic 20-node 1-D Gauss-Hermite approximation), wtp() (posterior median + quantile intervals), logsum() / consumer_surplus() (HMNL-only; probit Emax is roadmapped), elasticities() / diversion_ratios() (common-random-path perturbation engine including the outside option), recovery_table(), plus rhat() (split R-hat) and ppc_shares() diagnostics.
• Two new math articles: vignettes/articles/hierarchical_mnl_math.Rmd and hierarchical_mnp_math.Rmd; recovery demos in inst/simulations/hmnl_simulation.R / hmnp_simulation.R.

WESML sandwich inference for MNL and nested logit

• run_mnlogit() and run_nestlogit() now compute the robust (Huber–White / WESML) sandwich variance V = A^{-1} B A^{-1}, at full feature parity with run_mxlogit(). Both gain se_method = "sandwich" (bread = weighted negated Hessian, meat = weight-squared OPG) and se_method = "bhhh" (ordinary outer-product-of-gradients). run_mnlogit()’s se_method therefore now accepts "hessian" (default), "bhhh", or "sandwich"; run_nestlogit()’s accepts "hessian" (default), "numeric", "bhhh", or "sandwich".
• New C++ kernels mnl_bhhh_parallel() and nl_bhhh_parallel() accumulate the weighted outer product of per-individual scores. Their per-individual score is weight-free, so passing weights = w yields the BHHH information and weights = w^2 yields the sandwich meat. The NL kernel includes the full beta/lambda/delta score blocks (singleton-nest lambdas fixed to 1 contribute no score).
• run_mnlogit(), run_nestlogit(), prepare_mnl_data() and prepare_nl_data() gain a weights_col argument: a row-level weight column is collapsed to one weight per choice situation (validated constant within id). A choice-based-sampling provenance guard auto-adopts the recorded WESML weight column and errors rather than silently fitting unweighted under a WESML label; non-uniform weights under a non-sandwich se_method emit a warning.
• Behavior change: weighted fits now emit a steering warning recommending se_method = "sandwich" when non-uniform weights are supplied with the default ("hessian") or "bhhh" method; the "bhhh" case gets a sharper message explaining that BHHH/OPG is not a valid WESML correction (its meat is w^1, not w^2). Point estimates and standard errors are unchanged.
• wesml_vcov() now dispatches on choicer_mnl and choicer_nl (in addition to choicer_mxl), returning the post-hoc sandwich variance from a fit stored with keep_data = TRUE.
• summary() for MNL and NL fits now reports the standard-error method and any WESML weighting in the printed footer.
• Added “Choice-Based Sampling and WESML Weighting” sections to the multinomial logit and nested logit derivation vignettes.

Weighting safety hardening

• Weights are now validated to be finite and strictly positive in prepare_mnl_data(), prepare_nl_data(), and prepare_mxl_data() (covering both the weights= and weights_col= paths). Zero, negative, or non-finite weights previously could slip through and silently invalidate weighted and WESML sandwich inference (weight w enters the bread, w^2 the meat); they now error with an actionable message.
• Advanced-mode fits (input_data= passed directly to run_mnlogit(), run_nestlogit(), or run_mxlogit()) that carry WESML choice_sampling provenance but resolve to uniform weights now error instead of warning. The message explains how to proceed: bake the non-uniform WESML weights into input_data via prepare_*_data(weights=/weights_col=), or strip the provenance with attr(input_data, "choice_sampling") <- NULL for a deliberate unweighted fit. Convenience-mode behavior is unchanged.

Documentation

• Added eight vignettes: a getting-started tour (“Discrete choice from data to policy, in a dozen lines”), one per model (multinomial logit, mixed logit, nested logit, Bayesian multinomial probit, hierarchical Bayes), one on choice-based sampling and WESML weights, and one on standard errors (Hessian / BHHH / robust / cluster-robust, and when to use which).
• Added the mode_choice data set: the classic Greene & Hensher intercity travel-mode choice data (210 travellers x 4 modes), in choicer’s long layout, used by the getting-started vignette. ?mode_choice and the vignettes now document that the sample is choice-based (car under-sampled): slopes and WTP ratios are unaffected in the ASC-saturated logit, while constants, shares, and surplus levels inherit the design — see the WESML vignette for the correction.
• Added a pkgdown website configuration, including the model derivation notes as “The math behind choicer” articles.
• Expanded the identification discussions across the documentation: price endogeneity and the control-function route in the getting-started model-choice section (Petrin & Train 2010; blp() as the Berry 1994 inversion), what identifies the nested-logit dissimilarity parameters, the Keane (1992) covariance-identification caveat for the multinomial probit, and the classical MSL asymptotics (fixed-S bias, S growth conditions) in the mixed logit math note.

Mixed logit — on-the-fly digit-permuted Halton draws

• run_mxlogit() gains three new arguments: draws, seed, and scramble.
  • draws = "store" (default) keeps the existing behavior: a full K_w × S × N Halton cube is pre-materialized and stored in memory.
  • draws = "generate" activates the new mode: each individual’s S draws are computed on the fly in C++ from a compact seed, eliminating the O(N) eta_draws cube. This is the recommended choice when N is large or memory is constrained.
  • scramble = "permuted" (default when draws = "generate") applies one seeded permutation per dimension and base-digit position, shared across sequence indices. This is a deterministic position-wise digit permutation, not Owen’s nested-uniform scramble, and no standard randomized-QMC unbiasedness or replicate-error guarantee is claimed. The historical value "owen" remains as a deprecated alias for compatibility. scramble = "none" reproduces the randtoolbox sequence exactly.
  • seed sets the integer master seed for the on-the-fly generator; if NULL (default), a seed is drawn from R’s RNG so set.seed() governs reproducibility. Ignored when draws = "store".
• The draws_info field on choicer_mxl objects gains three new elements: mode ("store" or "generate"), seed, and scramble. Existing code that accesses draws_info$S, draws_info$N, or draws_info$K_w is unaffected; the new fields are NULL for objects fitted before this release.
• All post-estimation generics (predict(), elasticities(), diversion_ratios(), logsum(), consumer_surplus(), vcov()) propagate the fitted draw mode automatically — no user action required.
• Default behavior (draws = "store") is bitwise unchanged.

Bayesian models

• run_mnprobit() — Bayesian multinomial probit via Gibbs sampling with data augmentation (Albert & Chib 1993; McCulloch & Rossi 1994). Runs the non-identified chain with conjugate priors and reports identified quantities normalized per draw by sigma_11. New choicer_mnp posterior object with summary() (posterior mean, SD, credible intervals), coef(), vcov(), nobs(); math note in vignettes/articles/bayesian_multinomial_probit_math.Rmd
• C++ MCMC infrastructure built from scratch (src/rng.h, src/bayes_samplers.h): xoshiro256++/splitmix64 RNG with one stream per (iteration, observation) — draws are reproducible given the seed and a fixed OpenMP thread count (invariant across thread counts only up to floating-point reduction-order round-off, ~1e-15) — plus exact truncated-normal, multivariate-normal, Wishart (Bartlett), and inverse-Wishart samplers. The truncated normal picks the cheapest exact method per region (naive normal rejection in high-mass regions, Robert 1995 exponential rejection in the tail, inverse CDF for narrow intervals)
• The Gibbs chain runs inside a single persistent OpenMP region: the latent-utility sweep and mean refresh are work-shared across choice situations, the conjugate beta/Sigma conditionals run on the master thread between lightweight barriers with hand-rolled fixed-order linear algebra (no BLAS inside the region), and the truncated-normal conditional moments are hoisted per Sigma draw. On the _benchmarks/ MNP preset this samples ~2x faster than MNP::mnp() and ~3.5x faster than bayesm::rmnpGibbs() on one thread, and scales with threads on top
• simulate_mnp_data() — probit DGP returning a choicer_sim with truth on the identified scale; recovery_table() gains a choicer_mnp method (posterior mean/SD, normal-approximation credible intervals, sigma block); parameter-recovery walkthrough in inst/simulations/mnp_simulation.R

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

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.
Health stats visible at Monitor.