lcl-choice 0.1.23

A high-performance Python package for estimating latent-class conditional logit models.

LCL

LCL is a Python package for estimating latent-class conditional logit models. It runs an expectation-maximization (EM) algorithm on JAX, sharding the per-class M-steps across available accelerators, and returns a results object with clustered standard errors, counterfactual predictions, and Delta-method willingness-to-pay distributions.

Although I'm an economist by training, this package is intended for all social scientists who study household-level panel data: marketers, transportation researchers, operations researchers, political scientists, public policy and administration researchers, and others.

Key features

• LatentClassConditionalLogit — finite-mixture conditional logit with a fractional-response multinomial logit regression of class membership on demographics.
• ConditionalLogit — standard conditional logit, useful both as a baseline and as the inner kernel of the M-step.
• cv_optimal_classes — blocked K-fold cross-validation for choosing the number of latent classes. Folds are split at the decision-maker level, so no individual's choices appear in both training and held-out data.
• Counterfactual prediction — out-of-sample choice probabilities, expected consumer surplus, own- and cross-elasticities, and marginal willingness-to-pay broken out by demographic partitions.
• Inference — clustered sandwich covariance at the panel level and the Delta method for non-linear parameter combinations such as the value of time.

Types are enforced at runtime by jaxtyping and beartype. A wrongly shaped design matrix raises a readable error at the call site rather than a cryptic XLA trace.

Documentation

Full documentation — worked tutorials, an API reference, and a model-selection guide — is hosted at zeyveld.github.io/latent-class-conditional-logit.

Installation

The wheel is published on PyPI as lcl-choice (it imports as lcl):

pip install lcl-choice

If you plan to use a GPU, install the CUDA-matched JAX build first; see the JAX installation notes.

Quickstart

A two-class model on a small synthetic panel. The estimation tutorial provides a full example, including counterfactual fares and value-of-time partitions.

import numpy as onp
import polars as pl
import lcl
from lcl._struct import EMAlgConfig, MleConfig

rng = onp.random.default_rng(7)

# Two latent classes: one is price-sensitive, the other prefers quality.
n_panels, n_choices, n_alts = 200, 4, 3
true_class = rng.choice(2, size=n_panels, p=[0.55, 0.45])
beta_price   = onp.array([-1.8, -0.3])
beta_quality = onp.array([ 0.4,  1.6])

rows = []
for panel in range(n_panels):
    income = rng.normal()
    for case in range(n_choices):
        prices  = rng.uniform(0.5, 3.0, size=n_alts)
        quality = rng.uniform(0.0, 5.0, size=n_alts)
        u = (beta_price[true_class[panel]]   * prices
           + beta_quality[true_class[panel]] * quality
           + rng.gumbel(size=n_alts))
        chosen = int(onp.argmax(u))
        for alt in range(n_alts):
            rows.append({
                "panel": panel,
                "case":  panel * n_choices + case,
                "alt":   alt,
                "choice":  alt == chosen,
                "price":   float(prices[alt]),
                "quality": float(quality[alt]),
                "income":  float(income),
            })

df = pl.DataFrame(rows)

model = lcl.LatentClassConditionalLogit(num_classes=2, numeraire="price")
results = model.fit(
    data=df,
    alts_col="alt",
    cases_col="case",
    panels_col="panel",
    choice_col="choice",
    case_varnames=["price", "quality"],
    dem_varnames=["income"],
    em_alg_config=EMAlgConfig(maxiter=50, num_devices=1),
    mle_config=MleConfig(maxiter=40),
)

results.summarize_betas()
print(results)

A representative end-of-run printout:

Estimation time: 15.705 seconds
Information criteria: CAIC=1233.4, BIC=1227.4, adjusted BIC=1197.4

--- Table preview ---

┌──────────┬─────────────┬───────────────────────────┐
│ Variable │ Means (β's) │ Standard deviations (σ's) │
├──────────┼─────────────┼───────────────────────────┤
│ price    │ -1.124      │ 0.723                     │
│          │ (0.114)     │ (0.128)                   │
│ quality  │  0.905      │ 0.611                     │
│          │ (0.097)     │ (0.130)                   │
└──────────┴─────────────┴───────────────────────────┘

<LCLResults: 2 Classes | Converged | Log likelihood: -597.8 |
 CAIC: 1233.4 | BIC: 1227.4 | Adj. BIC: 1197.4>

The parentheses enclose Delta-method standard errors on the population moments. The class-specific β's themselves are available in results.em_res.structural_betas.

Roadmap

The estimator is fairly stable and the results object covers the cases I encounter in my own work. I'm hoping to make two extensions:

• Model selection. Blocked K-fold cross-validation is included but still marked experimental — expect refinements as I deploy this utility in my research.
• Documentation. A mathematical appendix and additional worked examples beyond Apollo's mode-choice data.

If there is a constraint, optimization routine, or post-estimation tool you'd like to see, open an issue.

Contributing

The project uses uv for dependency management:

git clone https://github.com/zeyveld/latent-class-conditional-logit.git
cd latent-class-conditional-logit
uv sync --all-extras --dev
uv run pytest tests/

Acknowledgments

LCL is built on JAX, Polars, equinox, jaxopt, jaxtyping, beartype, and formulaic. The differenced-design-matrix kernel at the heart of the conditional logit likelihood evaluation owes a particular debt to the xlogit package by Cristian Arteaga, JeeWoong Park, Prithvi Bhat Beeramoole, and Alexander Paz.

The documentation site is set in Luciole, a typeface designed for visually impaired readers by Laurent Bourcellier and Jonathan Perez in collaboration with the Centre Technique Régional pour la Déficience Visuelle and typographies.fr, released under CC-BY 4.0.

Citation

@software{lcl_2026,
  author = {Zeyveld, Andrew},
  title  = {LCL: Latent-Class Conditional Logit Estimation in Python},
  year   = {2026},
  url    = {https://github.com/zeyveld/latent-class-conditional-logit}
}