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drlate 0.3.1

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.

drlate 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.

drlate 0.1.0 (patch)

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.

drlate 0.1.0

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.

drlate 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.

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.