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Changelog

Source: NEWS.md

panglm 1.7.0

Plotting and teaching release: every fitted object has a plot method, the diagnostics, marginal effects and difference-in-differences results can be drawn directly with base graphics, and five real health economics panels ship with the package. Estimates are unchanged.

Diagnostics and coefficients

  • plot.panglm() gains which = "qq" (normal Q-Q plot of the Pearson residuals), "calibration" (observed proportion against the mean predicted probability in quantile bins, for binary outcomes) and "rootogram" (hanging rootogram for Poisson and negative binomial fits, including random-intercept fits through the marginal count distribution). The residual view uses Pearson residuals by default (type), adds a smooth, and uses a log axis for log-link models. Coefficient plots take parm, omit the intercept by default and size the margin to the term names.
  • residuals.panglm() gains type = c("response", "pearson").
  • New coefplot() compares the coefficients of several fitted models in one figure.
  • New plot methods for twopart(), panfmm() (coefficients by class and the posterior class probabilities), pangmm(), paniv() and panselect() fits; plot.panglm_hurdle() gains parm.
  • plot.panglm() gains which = "density": the observed kernel density against the model-implied density, with the point masses of Tobit censoring and two-part zeros; also for twopart() fits.
  • plot.panglm() gains which = "effects": the distribution of the estimated individual effects against the assumed mixing distribution, with an intercept-slope scatter for random slopes.
  • which = "correlation" draws the correlation matrix of the estimates as a heat map, for panglm(), twopart(), panfmm() and panhurdle() fits.

Individual effects

  • New fixef() returns the estimated fixed effects of within fits, and ranef() the posterior means or modes of the random effects of random fits, for random intercepts in every family and for random slopes. The methods also register on the nlme generics.

Marginal effects

  • New plot method for partialeffects() results.
  • effectcurve() gains scale = c("link", "response", "slope"): the average predicted outcome, or the average marginal effect, as the regressor varies, with delta-method bands, also for factor regressors; and by, curves for each level of a factor. The plot adds a rug of the observed values.

Difference-in-differences

  • attgt(), etwfe() and eventstudy() plots share one event-study layout, with uniform confidence bands drawn as visible bars behind the pointwise intervals. All three take min_e, max_e, ylim, main and legend.
  • Event-study plots and eventplot() gain style = c("classic", "points", "line"). The default classic style draws the estimates with capped intervals, the reference period as an open circle at zero and a dashed line labelled “Intervention” at event time 0; "line" joins the estimates over a shaded band. The arguments ref, onset, onset_label and caps control the markers.
  • plot(<attgt>) and plot(<etwfe>) gain type = "heatmap": the ATT(g, t) estimates by cohort and period on a diverging scale, with pre-treatment cells hatched and, with cells = TRUE, the estimates printed.
  • plot(<attgt>, type = "gt") draws the group-time effects, one panel per cohort.
  • New treatmentplot() shows the treatment timing of a staggered design.
  • New eventplot() compares event studies from several estimators on one axis. Legends wrap into columns when the panel is narrow.
  • New trendplot() draws the raw mean outcome by adoption cohort, by treated versus never treated, or on the event-time axis, with cluster-robust bands.
  • New panelplot() draws a unit by period heat map of a panel: the observation pattern, a numeric or categorical variable, and units sorted by adoption cohort.

Data

  • copd gains clinics, staggered adoption of a care programme, annual costs with structural zeros, a medication adherence proportion and an ordered symptom score, all simulated with known parameters.
  • New real panels for teaching: docvisits (German health care demand, Riphahn, Wambach and Million 2003), meps (two-year Medical Expenditure Panel Survey spending), medicaid (US states, Affordable Care Act Medicaid expansion and uninsurance, 2009-2019), healthspend (countries, health expenditure from the World Development Indicators) and cigar (US states, cigarette demand, Baltagi). Each help page states the source, the licence and the questions the data can teach.
  • New vignette vignette("panglm-datasets").
  • Lazy-loaded data are compressed with xz.

Bug fixes

  • panhausman(method = "classical") no longer reports p = 1 when V_fe - V_re is not positive definite and the statistic is negative: the signed statistic is kept, the p-value is NA and a warning points to method = "mundlak". The print method drops the stray space before commas.
  • Print methods use one sample-size line, n = <observations> groups = <individuals> (pangmm() printed the number of individuals as N), and keep every line within 80 characters (the pangmm() summary, the attgt() summary and the etwfe() footer were wider).
  • panglm() detects columns that are collinear up to rounding, not only exactly. With cre = TRUE, an age that rises one per year but is stored with a small rounding error (e.g. pglm::HealthIns) gave offsetting coefficients of order 1e5 with no warning. Columns are now scaled by their own norm before the rank check, and a near dependency is dropped with the usual “rank-deficient model matrix” warning. Exact dependencies drop the same columns as lm(). This applies to every model and family.
  • pangmm(model = "onestep") no longer warns that the second-step weighting matrix is singular: a one-step fit uses it only for the Hansen statistic, through a generalized inverse as in plm::pgmm(). Two-step fits keep the warning.
  • panhurdle(family = "negbin") no longer stops with “NAs are not allowed in subscripted assignments” when the NB2 shape parameter runs off to infinity (counts no more dispersed than Poisson): log P(Y = 0) is computed as -theta * log1p(mu / theta), which stays finite.
  • panhurdle() drops time-invariant regressors with a warning, as panglm(model = "within") does, instead of failing with “system is computationally singular”. Groups whose positive counts are all 1 (no finite intercept) are dropped from the count part with a message, and a binary outcome stops with a clear error.
  • panglm(model = "random", family = "tobit") now converges on outcomes in raw units such as costs in dollars. The quasi-Newton search used to stop silently short of the maximum (on meps$totexp the log-likelihood was 29 units below it and sigma 22% off) because the slopes and the log standard deviations differed in scale by orders of magnitude. Both Tobit fits now maximize the likelihood of y / c (c a power of two near sd(y)) with standardized regressors and map the estimates back, so that coefficients, sigma_e, sigma, their covariance and the log-likelihood (which loses log c per uncensored observation) do not depend on the units of y. The random-effects likelihood and its exact scores are computed in C++: the meps fit takes 0.4 s instead of 210 s.

Performance

  • twopart(correlated = TRUE) is much faster: the joint likelihood and its exact gradient (including the dependence of the adaptive quadrature mode and scale on the parameters) are computed in C++, replacing numerical differences of an R likelihood. A meps fit on 6,000 person-years takes about 5 seconds instead of 5 minutes. Estimates are unchanged up to the optimizer tolerance, and the likelihood matches GLMMadaptive::mixed_model(family = hurdle.lognormal()) at the same parameters.
  • The panhurdle() count part is fit by Newton-Raphson with analytic derivatives and the group intercepts eliminated, instead of a dense dummy matrix (and, for NB2, BFGS with a numerical Hessian). A fit that took minutes on a few hundred docvisits individuals now takes well under a second, the full panel about one second. Estimates match glmmTMB truncated Poisson and NB2 fits with group dummies; the NB2 standard errors, which came from a not fully converged fit, change.
  • Random-intercept binomial, ordered and Tobit fits by adaptive quadrature are faster: parameter values with sigma outside [1e-11, 7e10], which the first quasi-Newton steps can visit, are rejected without a mode search (each such evaluation used to cost as much as 200 ordinary ones), each individual’s mode search starts from its mode at the previous evaluation and takes one pass over the rows per Newton step, and the binomial fit starts from a few BHHH steps. The probit model of doctor on docvisits (27,326 rows) takes 4 s instead of 37 s; the estimates agree to 1e-7.

panglm 1.6.2

Speed release for large panels. Estimates are unchanged: the optimizations either reproduce the previous computations exactly or change results only at the level of floating-point rounding, and the tests and validation scripts pass unchanged.

Performance

  • Two-way demeaning computes the group means in one pass over the data instead of a parallel reduction that allocated one accumulator per thread. The two-way Gaussian fit demeans the outcome and the regressors once and reuses the result for the check of which columns are identified.
  • The fixed-effects Poisson and random-effects Poisson and negative binomial Newton-Raphson fitters reuse the gradient and Hessian of the accepted step instead of evaluating the same point twice.
  • Random-effects negative binomial: the gamma, digamma and trigamma differences of the likelihood are computed by their finite-sum recurrences for counts up to 50, skipped for zero counts, and spread over threads. A fit on 250,000 observations is about seven times faster.
  • pangmm(): the individual sums of the GMM moments and the Windmeijer correction use block sums and two matrix products instead of one grouped sum per regressor.
  • The check for duplicated (individual, time) pairs in panglm(), pangmm(), attgt() and predict() compares neighbouring sorted rows instead of pasting every row into a string.
  • attgt() and did(): the checks that weights, clusters and the treatment timing are constant within individuals no longer call R once per individual.
  • attgt() drops the row names of the outcome and design before subsetting them, and fits the propensity score with a compiled logistic IRLS that follows glm.fit() step by step. Unusual fits (nearly collinear design, no convergence, fitted probabilities of 0 or 1) still go through glm.fit(). On 50,000 individuals the "dr" and "ipw" estimators are about twice as fast.
  • Gaussian fits with the identity link are computed by one weighted least squares solve instead of running the IRLS loop to convergence.
  • panglm() no longer copies the outcome and design when the data are already sorted by individual and time.

Bug fixes

  • attgt(): when the individual index is a factor, individuals whose rows had all been removed earlier (for example for missing values) were also counted among the individuals dropped to balance the panel (warning message and dropped$unbalanced). Only individuals still present are counted now. Estimates are unaffected.

panglm 1.6.1

Correctness release. Every fix is covered by the tests and the validation scripts.

Changed behaviour

  • Random-intercept binomial (logit, probit) and ordered (logit, probit) models are now integrated by adaptive Gauss-Hermite quadrature centred at each individual’s mode, instead of non-adaptive quadrature. Estimates change, most for large random-effect variances or long panels. At the default R = 21 the fits match lme4::glmer(nAGQ = 21) and ordinal::clmm(nAGQ = 21) to about 1e-5; R = 1 is now the Laplace approximation (glmmTMB, clmm(nAGQ = 1)).
  • panqr(model = "mmqr"): the analytic covariance (vcov = "analytic", the default) is now cluster-robust by individual. The influence functions of the location, scale and quantile steps are summed within each cluster and V = G/(G-1) sum_g psi_g psi_g', where G is the number of clusters. The formula of Machado and Santos Silva (2019), which assumes independent observations and can badly overstate the standard errors in short panels, is kept as vcov = "iid". A new cluster argument names another clustering column. Standard errors of existing MM-QR fits change.
  • Gaussian random effects on unbalanced panels now use the Swamy-Arora variance components of plm (random.method = "swar"), so estimates on unbalanced panels change and match plm::plm(model = "random"). Balanced fits are unchanged. A negative individual-effect variance is set to zero with a warning.
  • The one-way and two-way fixed-effects logit are fitted by Newton-Raphson with the exact analytic Hessian of the conditional likelihood instead of BFGS with a finite-difference Hessian. Coefficients and standard errors match survival::clogit() to about 1e-13, and the fit is faster than clogit at N = 10,000 and 50,000.

Bug fixes

  • panglm() accepts base R family objects (gaussian(), poisson(), binomial("logit"), binomial("probit"), Gamma("log")) and maps them onto the panglm families; other stats families stop with a message naming the panglm constructor.
  • attgt(), did(), eventstudy() and etwfe() no longer fail when the individual index is a factor with unused levels (e.g. after subset()).
  • pangmm() drops regressors that are not identified in the transformed equation (for example age in a balanced annual panel) with a warning, instead of stopping with a singular-system error.
  • Two-way demeaning (effect = "twoways") by alternating projections now warns if it reaches the iteration limit before converging.

Documentation

  • Two-way demeaning is described as iterative, converging to tolerance, rather than exact.
  • The cre test is described as a Hausman-type (Mundlak) test that is robust only with vcov = "cluster" (or "HC1" for heteroskedasticity alone).
  • The beta family validation is now in validation/validate-beta.R (betareg, GLMMadaptive, glmmTMB and numerical integration).
  • The README, vignettes and help pages describe what each function fits rather than listing the packages it is checked against; the checks are summarised in the validation article on the package website.

panglm 1.6.0

This release collects all changes since 1.1.4, the previous CRAN version. Every new estimator is covered by the tests and the validation scripts; the validation article on the package website summarises the checks.

Deprecations and changed behaviour

New estimators

  • model = "between": the between (individual means) estimator for the Gaussian family (plm).
  • tobitfamily(left, right): censored outcomes, pooled or with a random intercept (AER, censReg).
  • orderedfamily(): ordered logit and probit, pooled, random intercept, and the fixed-effects ordered logit by “blow-up and cluster” (Baetschmann, Staub and Winkelmann 2015).
  • betafamily(): beta regression for outcomes in (0, 1), pooled or with a random intercept (betareg, glmmTMB).
  • gammafamily(): gamma regression with log link for costs, pooled, fixed effects or random intercept.
  • Two-way fixed-effects logit: model = "within", family = "binomial", effect = "twoways" (survival::clogit).
  • Random slopes: panglm(model = "random", random = ~ time), correlated random slopes with an unstructured covariance. Gaussian fits use exact ML with any number of slopes (lme4); Poisson, logit, probit, negative binomial and gamma fits use adaptive Gauss-Hermite quadrature with any number of slopes (glmmTMB, GLMMadaptive).
  • paniv(): panel instrumental variables (fixed effects, G2SLS or EC2SLS random effects, pooled 2SLS) and the Hausman-Taylor estimator, with first-stage F statistics and Sargan/Hansen tests (plm).
  • pangmm(): Arellano-Bond difference and Blundell-Bond system GMM, one- or two-step with Windmeijer standard errors, AR and Hansen tests, collapsed instruments and lag limits; it warns when instruments outnumber individuals (plm::pgmm).
  • panselect(): the Wooldridge (1995) correction for attrition or death in cohort studies (glm, plm, sampleSelection).
  • panqr(): panel quantile regression by Canay (2011), MM-QR (Machado and Santos Silva 2019) or pooling, with a plot() method (quantreg).
  • panfmm() and panfmmselect(): latent class GLMs with class membership constant within individual, Poisson, NB2, Gaussian, logit and probit components, weights, offsets and a concomitant model (flexmix).
  • twopart(): two-part models for semicontinuous costs, with correlated random intercepts (correlated = TRUE).
  • did() and eventstudy(): two-way fixed-effects difference-in-differences and event studies for Gaussian, Poisson, negative binomial and binomial (conditional logit) outcomes (fixest, survival::clogit).
  • attgt(): Callaway and Sant’Anna (2021) group-time ATTs with aggregations, pre-trend test, weights, extra clustering and a multiplier bootstrap with uniform bands (did).
  • etwfe(): the Wooldridge (2023) extended two-way FE estimator for linear, Poisson and logit outcomes, with weights, xvar and ivar (etwfe).

New features

  • lag(), lead() and diff() in the formulas of panglm(), paniv(), panqr() and panfmm() are taken within each individual, with NA at the start of each panel (plm). Model functions that cannot support them stop with an informative error.
  • cre = TRUE: correlated random effects (Mundlak) for pooled and random-effects fits of every family, with a joint test in summary().
  • dynamic = TRUE: lagged and initial outcomes with Mundlak means (Wooldridge 2005 initial conditions).
  • partialeffects(): average partial effects with delta-method standard errors.
  • Weights: pooled and random-effects fits of every family, twopart(), panfmm(), attgt() and etwfe() accept weights, given as a vector or a column name. offset also accepts a column name.
  • Splines: jointtest(), lineartest(), effectcurve() with a plot() method, and model.matrix() that keeps the knots on new data.
  • panhausman(method = "mundlak"): the regression-based Hausman test.
  • pangmm() accepts vcov = "cluster".
  • tidy() and glance() for every model class, so modelsummary works; glance() reports theta for negative binomial fits and an adjusted R-squared that counts absorbed fixed effects (fixest).
  • predict() for pangmm, paniv and panselect; summary(), vcov(), nobs(), confint() and logLik() for event studies; confint() for twopart; full methods for panhurdle() fits; one print style across classes.
  • options(panglm.threads) caps the parallel threads, which are also capped by OMP_THREAD_LIMIT and at 2 under R CMD check. OpenMP is no longer enabled.

Robustness

  • Maximum likelihood fits standardize the design internally and warn when the Hessian is not negative definite, standard errors are missing or the gradient is not zero. Badly scaled regressors no longer break beta fits.
  • The random-effects negative binomial warns when its dispersion diverges (no overdispersion beyond the individual effect, so the model reduces to the random-effects Poisson).
  • Random-slope fits warn when the random-effects covariance is singular, for every family.
  • The random-effects Tobit is stable in the far censored tail.
  • The random-effects beta likelihood is about 3 times faster.

Bug fixes

  • Robust and cluster covariances: the pooled negative binomial used the Poisson score, and the two-way fixed-effects Poisson ignored the time effects; both now match sandwich and fixest::fepois().
  • A fit made with vcov = "HC1" or "cluster" overwrote its model-based covariance; vcov(fit, type = "classical") now returns it.
  • panhausman() always uses the model-based covariances in the classical contrast.
  • Negative binomial summaries print theta and its standard error instead of “Dispersion parameter: 1”.
  • did() and eventstudy() summaries show the user’s call.
  • paniv(model = "random") no longer warns about regressors it keeps.
  • A factor individual index works.
  • The copd help page documents all 10 columns.

Dependencies and documentation

  • data.table is no longer a dependency.
  • Roxygen markdown is enabled and every help page was reviewed; functions are grouped into families with cross-links.
  • New vignettes: a getting started guide, four task guides (outcomes, endogeneity, policy evaluation, heterogeneity) and a technical reference.
  • A pkgdown site and a rewritten README.

panglm 1.1.4

CRAN release: 2026-09-22

panglm 1.1.3

  • Added method references (Hausman 1978, Chamberlain 1980, Allison and Waterman 2002, Croissant and Millo 2008) to the DESCRIPTION as <doi:...> links.

panglm 1.1.2

Bug fixes

  • model = "random", family = "gaussian" always reported logLik/AIC/ BIC as NA, even though fit_random_gaussian() already estimates the Swamy-Arora variance components (sigma_v2, sigma_mu2) needed to compute a real likelihood - they were simply discarded. Added the closed-form Gaussian random-intercept log-likelihood (Sherman-Morrison identity on Omega_i = sigma_v2 * I + sigma_mu2 * J), using the already- estimated coefficients and variance components. Cross-validated: on the bundled copd data, logLik(random) closely tracks logLik(pooling) on the same formula, as expected since random-effects GLS nests pooled OLS as sigma_mu2 -> 0.
  • model = "within" (one-way and two-way) had the same loglik = NA_real_ gap. By the Frisch-Waugh-Lovell theorem the demeaned-regression residuals are algebraically identical to the full dummy-variable-regression residuals, so the standard OLS ML Gaussian log-likelihood applies directly - added, using the npar (regressors + fixed-effect count + 1 dispersion parameter) that panglm_parameter_count() already computed for these branches but previously had no likelihood to pair with.

New features

Bug fixes

  • panglm(model = "random", effect = "individual", family = "gaussian") printed a raw solve(): system is singular; attempting approx solution from Armadillo whenever a covariate was time-invariant (e.g. baseline sex alongside a pre/post indicator) or the panel was balanced with a deterministic time indicator (e.g. any two-wave pre/post design) - both make the intermediate within/between regressions used to estimate the Swamy-Arora variance components exactly rank-deficient. The estimates were already correct (verified against lme4::lmer), but the warning bypassed R’s condition system entirely. Switched those two intermediate regressions to stats::lm.fit(), which handles rank deficiency via pivoted QR the same way lm() does, with no near-singular solve and no raw stderr output.

panglm 1.1.0

CRAN submission candidate. Adds a bundled synthetic dataset, copd (a simulated COPD follow-up panel), and runnable @examples to every exported function and documented method.

  • Added plot.panglm() for coefficient, residual, and fitted-value views, and plot.panglm_hurdle() for the two hurdle components.
  • Added zero-truncated NB2 positive-count models to panglm_hurdle() through count_family = "negbin".
  • Added HC1 and cluster-robust covariance estimates for one-way conditional binomial fixed-effects models.
  • Reorganized the package vignette as a methodological account of model specification, estimation, inference, diagnostics, and scope.
  • Completed fitted-value and prediction semantics across estimators. Random-effects response predictions integrate over the fitted mixing distribution, fixed-effects predictions include known panel effects, and exact conditional binomial fits expose conditional inclusion probabilities. Fitted values and residuals now preserve the input row order for unsorted panels.
  • Corrected likelihood parameter accounting. logLik() now counts fitted dispersion, shape, variance, and absorbed fixed-effect parameters where they belong to the reported likelihood. Gaussian pooled likelihoods use the maximum-likelihood variance while retaining the unbiased residual variance for covariance estimation. glance() now reports AIC, BIC, and the likelihood degrees of freedom.
  • Corrected sandwich inference for exact conditional binomial models. HC1 now operates on independent stratum scores, and custom clusters are required to preserve complete conditioned strata. Custom cluster vectors are now correctly reordered when a panel was supplied out of index order.
  • Revised the fixed-effects NB2 description to state that the dummy-variable estimator remains subject to finite-T incidental-parameter concerns in short panels.
  • Hardened model-frame handling. Factor interactions are retained, unidentified columns are reported and removed before numerical fitting, na.exclude restores fitted values and residuals to the input length, and panel indexes are checked for missing or duplicate individual-time keys. Nontrivial weights and nonzero offsets now fail explicitly instead of risking an apparently successful fit that ignores them.

panglm 0.5.0

First CRAN submission candidate. Consolidates the estimators, inference, and zero-inflated-count support added since 0.1.0 (see below); no functional changes since the last version bump beyond package-metadata and documentation cleanup for submission (URL/BugReports fields, vignette housekeeping).

panglm 0.1.0

Initial release.

Estimators

  • model = "pooling": gaussian/poisson/binomial GLM via IRLS, and genuine NB2 (negative binomial) via theta-profiled IRLS – matches MASS::glm.nb() exactly. (family = "negbin" previously silently aliased to Poisson at every model level; this is fixed everywhere below.)
  • model = "within": gaussian (exact demeaning, one-way or effect = "twoways"), poisson (conditional MLE, one-way or effect = "twoways" via outer-IRLS/inner-weighted-FWL – the algorithm behind fixest::feglm()), negative binomial (Allison & Waterman 2002 unconditional dummy-variable estimator – matches fixest::fenegbin() exactly; tractable for small-to-moderate N, not intended for very large panels), and binomial (exact conditional logistic regression, Chamberlain 1980 – matches survival::clogit(method = "exact"), no pglm equivalent). effect = "twoways" is available for gaussian, poisson, and negbin (the latter two via outer-IRLS/inner-weighted-FWL, matching fixest::fenegbin() exactly); not yet for binomial (no general closed-form two-way conditional logit exists).
  • Groups with an all-zero outcome are now dropped (with an informative message()) before fitting the Allison-Waterman FE-NB2 estimator – their dummy-variable intercept is unbounded under the log link, the same boundary case model = "within", family = "binomial" already screened for. Previously left in, this could contaminate the shared covariate coefficients via the joint solve, not just the offending group’s own intercept.
  • model = "random": gaussian (Swamy-Arora), poisson (Poisson-Gamma, a single dispersion parameter), negative binomial (a genuinely different, 2-parameter beta-negative-binomial mixture matching pglm’s lnl.negbin.R exactly – previously aliased to the Poisson-Gamma model), and binomial (Gauss-Hermite quadrature) random effects. The random-effects Poisson fit now seeds from the pooled-Poisson MLE plus a method-of-moments dispersion estimate, and uses Levenberg-Marquardt/ridge damping (with a warning when it engages) instead of erroring on a singular Hessian – verified on heavily zero-inflated, extremely skewed panels that previously crashed.

Inference

  • vcov(fit, type = "HC1" | "cluster") for pooled/within models, including effect = "twoways" (gaussian) and within negbin (Allison-Waterman): the sandwich correction uses the full covariate+dummy score and bread (all (k+G) parameters), not just the covariate block, since the dummy intercepts are jointly estimated and contribute sampling variability of their own. Random-effects models intentionally keep model-based (information-matrix) SEs only, matching lme4/glmmTMB convention.
  • confint.panglm() – Wald intervals using whichever vcov the fit currently carries.
  • panglm_dispersiontest() – Pearson chi-squared/df overdispersion test for poisson/negbin fits with available fitted.values. Observations with a zero or unavailable fitted value (e.g. rows in an all-zero group screened out of a fixed-effects negbin fit) are excluded from the statistic rather than producing a 0/0 = NaN; the excluded count is reported as n_excluded on the returned object.
  • panglm_hausman() for FE-vs-RE specification testing.
  • tidy()/glance() methods registered against generics::tidy/glance for broom/modelsummary compatibility.

Zero-inflated counts

  • panglm_hurdle(): a fixed-effects hurdle model for panels with structural zeros (a pattern that shows up as extreme, family-invariant overdispersion under either family = "poisson" or family = "negbin" in panglm()). Fits 1(y>0) via the existing conditional-logit FE estimator, and a zero-truncated Poisson or NB2 FE model (an Allison-Waterman-style dummy-variable estimator, Newton-Raphson with exact Fisher information) on the y>0 subsample. The truncated-Poisson math (no fixed effects) matches pscl::hurdle()’s count part exactly. Both zero-truncated likelihoods are validated against pscl::hurdle().

Known limitations

  • effect = "twoways" not yet available for binomial (no general closed-form two-way conditional logit exists).
  • No between effects.
  • No ordinal/tobit families.
  • Not yet on CRAN.