Changes in version 0.3.1 (2026-06-24)                  

First CRAN release. (The 0.3.0 submission was revised at CRAN's request
to document the return value of the confint() method; the feature set
below is unchanged.)

New outcome and treatment model families, completing parity with the
Stata lateffects omodel/tmodel options:

  - omodel gains "probit" (binary outcome, probit link) and "flogit" /
    "fprobit" for fractional outcomes in [0, 1] (e.g. proportions or
    rates). tmodel gains "probit". The fractional families share all
    estimation with their binary counterparts and only relax the
    response to the unit interval. They reuse the probit/logit
    quasi-likelihood scoring already validated for the instrument
    propensity score, and the test suite checks them against first
    principles: the fractional families coincide with their binary
    counterparts on a 0/1 response, and every fit reproduces the
    corresponding weighted glm estimate.

Postestimation diagnostics mirroring the Stata lateffects suite
(StataNow):

  - complier_means() reports population versus complier covariate means,
    the complier averages computed with the normalized Abadie-kappa
    weights (Stata's estat compliers). kappa_weights() returns those
    weights (the genkappa object) for use in other complier summaries.
  - balance_test() implements the Imai and Ratkovic (2014)
    overidentification test for whether the instrument propensity score
    balances the covariates (Stata's latebalance overid); cluster-robust
    when the fit is.
  - balance(detail = TRUE) adds IPW-weighted arm means and unweighted
    and weighted variance ratios alongside the standardized mean
    differences (Stata's latebalance summarize).
  - plot() gains type = "balance_density" (covariate kernel densities by
    instrument arm, raw versus weighted; Stata's latebalance density)
    and a geom = "density" option for type = "overlap" (Stata's
    lateoverlap).

These diagnostics are verified against their standard references: the
Abadie-kappa identity for the complier means, the Imai and Ratkovic
(2014) statistic for the balance test, and the bootstrap for the
standard errors.

                        Changes in version 0.2.0                        

  - New kappa-weighting estimators of the LATE from Słoczyński, Uysal,
    and Wooldridge (2025, JBES 43(1), 164-177): method = "kappa"
    (tau_a), "kappa0" (tau_a,0), and "kappa10" (tau_a,10), validated
    against the Stata kappalate command. Cluster-robust SEs, sampling
    weights, the bootstrap, and (for "kappa"/"kappa0") Fieller
    confidence sets carry over from the existing machinery.
  - Printed output now shows the kappalate names for the IPW estimators:
    normalized IPW is tau_u, unnormalized IPW is tau_a,1.
  - New ivmodel = "probit" (kappalate's zmodel(probit)) for the
    weighting estimators ("ipw" and the kappa methods), completing
    coverage of the kappalate command's options.
  - drlate_compare() now reports each kappa estimator's own
    normalization in the normalized column, and ?drlate documents that
    the kappa denominators are kappa-weight means — estimating the same
    complier share as the IPW first-stage contrast, but as a different
    sample statistic.

                        Changes in version 0.1.0                        

Changes from an internal econometric audit (Monte Carlo evidence in
data-raw/mc-review.R and data-raw/mc-weak2.R):

  - Correctness (deliberate divergence from Stata 1.0.0): the
    unnormalized LATT-AIPW estimator now computes the treated share with
    sampling weights. Stata uses an unweighted mean there, which leaves
    its w1 moment condition nonzero under pweights and invalidates the
    joint variance; with uniform weights the two coincide exactly (all
    validated configurations are unaffected).
  - Fieller sets handle the degenerate-quadratic regimes explicitly
    (half-line when denom^2 = q * V_dd, single-point tangency) instead
    of collapsing them into "whole line", and the complement-set print
    states that the set is unbounded.
  - The weak-instrument advisory now triggers at first-stage F < 10 (|z|
    < 3.16) instead of |z| < 2, and the printout reports z^2 ~ F.
  - Cluster bootstrap warns when there are fewer than 30 clusters;
    documentation notes that failed bootstrap draws concentrate where
    identification is weak (prefer the Fieller set in that regime) and
    that seeded results are reproducible per fixed number of cores.
  - drlate_compare() documents that IPW/RA rows use reduced adjustment
    sets (estimator and specification change together) and de-duplicates
    rows after normalization auto-switching.
  - dr_hausman() documents that the LATT and ATT halves adjust on the
    instrument- and treatment-equation covariates respectively.

Extensions beyond the Stata original:

  - Diagnostics: plot() methods for instrument propensity score overlap,
    covariate balance (love plot), and implied-weight distributions;
    balance() returns standardized mean differences; print()/summary()
    report first-stage strength and flag weak instruments.
  - Bootstrap inference: drlate(vcov = "bootstrap") provides
    nonparametric bootstrap standard errors and percentile confidence
    intervals (cluster bootstrap when cluster is supplied), with
    optional parallelism.
  - Weak-instrument-robust inference: confint(method = "fieller")
    inverts the joint test of the numerator and denominator, returning
    bounded, complement, or whole-line confidence sets as appropriate.
  - DR Hausman test: dr_hausman() implements the doubly robust test of
    unconfoundedness from Słoczyński, Uysal & Wooldridge (2022,
    Section 5) — proposed in the paper but not available in the Stata
    package — with an analytic standard error from one jointly stacked
    moment system.
  - Estimator comparison: drlate_compare() runs IPWRA/IPW/AIPW/RA in one
    call with a comparison table and dot-whisker plot.
  - Documentation site: pkgdown website with the primer vignette
    featured, deployed via GitHub Actions.

                     Changes in version 0.0.0.9000                      

  - Initial R port of the Stata package drlate v1.0.0 (SSC S459708).
  - LATE and LATT estimation via IPWRA, IPW, AIPW, and RA.
  - Linear, logit, and Poisson outcome/treatment models.
  - Logit (MLE), CBPS, and IPT instrument propensity score models.
  - Joint sandwich inference across all estimation stages, with robust
    and cluster-robust variants and sampling weights.
  - Overlap diagnostics (pstolerance, osample).
  - Golden-fixture test harness for numerical equivalence with Stata:
    estimates match to ~1e-9 and standard errors to ~1e-6 across all 33
    validation scenarios.