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Changelog

Source: NEWS.md

childpen 0.3.0

Labels, arguments and help pages now follow the paper. This release changes the output format; there are no deprecated aliases. All estimates and standard errors of rows that exist in both versions are unchanged (checked with dev/snapshot_compare.R against v0.2.3 output).

Breaking changes

  • Method and estimand labels: NTD_Conv / theta is now Conventional / gap_theta; NTD_New / Delta_rho is now NTD / rho_effect; TD / ATE is now TD / gap_ATE; TD_E_LOGS / Delta_rho is now TD_E_LOGS / rho_effect; TD_PR / theta_pre is now TD_PR / gap_theta_pre.
  • The fathers’-null rows TD_Null and NTD_Conv_Null are removed; see bias_bounds().
  • multiple_treatment_group_analysis(): max_age is now max_control_group (it bounds the control group d’, the paper’s D_max) and min_age is now min_pre_age (it bounds the target age of pre-treatment cells). periods_post must be one number H (event times 0..H); periods_pre = 0 now means no pre-treatment cells (it used to run e = -1 and -2).
  • aggregate_estimands(): agg_type is "weighted_avg" (was "avg_of_ratios" and "gender_ineq") or "ratio_of_avgs".

New

  • bias_bounds(): the paper’s bias-bounding exercise (Proposition 2), the bias-corrected conventional gap at assumed fathers’ effects (default theta_m = c(-0.1, 0.1)) for every post-treatment cell, with influence-function standard errors.
  • Accounting rows for NTD and TD_E_LOGS: rho_obs, rho_cf, share_parenthood (parenthood’s share of the gender gap) and share_other, with standard errors; also aggregated by aggregate_estimands().
  • aggregate_estimands() aggregates the TD gap (gap_ATE), accepts weights = "uniform", and reports t and p.
  • single_treatment_group_analysis() returns the same columns as multiple_treatment_group_analysis() (d, dp, a, event_time, control_offset, ci_l, ci_h, t, p); results carry control_offset (= dp - d), and multiple_treatment_group_analysis() attaches attr(, "skipped").
  • Articles follow the paper, in order: Notation and output, Estimation (every estimator, its influence function, and the assumption behind each row), Aggregation, and Replicating the paper (the paper’s main figures, including the accounting exercise, on simulated data; on the site only).

Input checks and messages

  • Gender labels outside a recognised set (“1”/“0”, “female”/“male”, “f”/“m”, …) are an error; before, text codes such as “1”/“2” silently swapped genders.
  • Rows with missing earnings are dropped with a message (before, one missing value made every estimate NA); a missing or infinite age at first birth marks a person who never has a child.
  • Duplicate (id, age) rows, gender or age at first birth varying within a person, and missing ids are errors; messages name the argument to fix.
  • A data.table input is no longer modified in place, and the column-name arguments now work for data.table input in single_treatment_group_analysis().
  • Invalid comparisons are errors: a control group already treated at the target age (dp <= a), dp <= d, pre < 1, or a target age equal to the baseline.
  • Progress is reported with message(), one line per treatment group; cells that cannot be estimated are summarised in one message.
  • aggregate_estimands() stops when results lacks the influence functions (it used to fall back to standard errors that treat treatment groups as independent).
  • aggregate_estimands() stops when no weight name matches a treatment group and warns about groups without a weight; n_groups counts groups with positive weight.
  • simulate_data() has a new design that shows the paper’s patterns: career entry later and growth steeper for later first births (parallel trends fails in the paper’s direction; pre-treatment DID estimates grow in magnitude with the distance to the control group), effects of parenthood that differ by age at first birth and fade with event time, and a gender earnings ratio that falls with age (so NTD holds approximately, TD-E-LOGS exactly). The true values for every treatment group and age are attached (attr(, "truth")). The simulated data differ from earlier versions. It also works with a single treatment group and checks its inputs.
  • multiple_treatment_group_analysis() stops when no cell can be estimated, and aggregate_estimands() stops when no requested method is in results or nothing can be aggregated (both used to return an empty data.frame).
  • A non-numeric outcome column is an error (it used to be coerced).
  • Row order: multiple_treatment_group_analysis() sorts by d, event_time, dp; aggregate_estimands() by method, agg_type, event_time.

Speed and memory

  • Each 2x2 comparison now uses only the rows of its two groups at the baseline and target ages, and stores influence functions only for the persons in them; the influence functions and variances are still scaled by the full data’s number of rows and persons. In a 280,000-row test, multiple_treatment_group_analysis() runs about 6 times faster (191 s to 33 s) and the stored influence functions take about 5 times less memory (418 MB to 76 MB). Results are unchanged up to floating-point rounding (dev/perf_check.R).

Bug fixes (standard errors change)

  • aggregate_estimands() with weights = "sample" (the default): the term for the estimated treatment-group shares in the influence function (the paper’s estimation and inference appendix) divided by the number of persons in the aggregated groups instead of their share, so it was about n times too small and the standard errors treated the weights as known. It now follows the paper’s formula. Point estimates are unchanged; standard errors are larger when treatment-group estimates differ (in a test design with heterogeneous effects, the old standard error was 22% below a person bootstrap, the new one matches it). ratio_of_avgs now also includes the share term. Standard errors with uniform or fixed weights are unchanged.
  • aggregate_estimands(include_pre = TRUE) failed (or pooled control groups as if they were treatment groups). Pre-treatment cells are now aggregated separately for each control offset, reported in the new control_offset column.

Proportional triple differences

  • Two estimators from the paper’s online appendix “Proportional DID and TD”, built on the multiplicative imputation of counterfactual earnings, with cluster-robust influence-function standard errors: TD_E_LOGS (estimand rho_effect, the effect of parenthood on the gender earnings ratio, as identified under TD-E-LOGS) and TD_PR (estimand gap_theta_pre, the gender gap in effects relative to pre-birth earnings, as identified under TD-PR). Both are carried by multiple_treatment_group_analysis() and aggregated by aggregate_estimands(); their standard errors are validated by Monte Carlo (dev/sims/td_sim_results.md in the source repository).

childpen 0.2.3

CRAN release: 2026-06-02

  • Replaced T with TRUE in multiple_treatment_group_analysis() default for verbose parameter, as flagged by CRAN review.
  • Fixed aggregate standard errors in aggregate_estimands() to correctly implement the paper’s Appendix G inference formulas.
  • multiple_treatment_group_analysis() now routes skipped-cell diagnostics (e.g. empty subgroups) through the verbose argument; previously these printed unconditionally to the console.
  • simulate_data() now saves and restores the caller’s RNG state on exit, so it no longer alters the global random stream. Generated output is unchanged. seed = NULL is now accepted to draw from the current RNG without reseeding.

childpen 0.2.2

  • Rewrote simulate_data() with a self-contained DGP: lifecycle earnings, gender gap, selection on treatment timing, and gendered treatment effects. No internal data dependencies.
  • Removed internal datasets (male_profiles, female_params, variance_params) and related documentation.
  • Rewrote vignettes to focus on estimator usage and interpretation rather than truth/counterfactual comparisons.
  • Fixed CRAN check: restricted data.table threads to 1 in all vignettes and tests to comply with CRAN’s 2-core policy.
  • Added inst/CITATION for the underlying paper.

childpen 0.2.1

childpen 0.2.0

  • Added aggregate_estimands() to aggregate individual estimands into population-level child penalty estimates.
  • Added vignettes: NTD identification, TD identification, DID estimation, estimation, validation tests, aggregation, and notation reference.
  • Added comprehensive testthat test suite (72 tests across all exported functions).
  • Added GitHub Actions R CMD check CI workflow.
  • Renamed NTD estimands for consistency with the identification framework in Leventer (2025).
  • Fixed prep_data_table() to handle haven_labelled columns from survey data imported with haven.

childpen 0.1.0