NEWS.md

In zmctp: Zero-Modified Complex 'Tri-Parametric' Pearson Distribution for Overdispersed Count Data

zmctp 0.1.3

Bug fixes

• Fixed a starting-value inconsistency in ctp.fit() that could cause optim() to silently converge to a degenerate local optimum for moderately overdispersed data. a_start and gama_start are derived independently from the sample mean/variance, with nothing guaranteeing gama_start > 2*a_start + 2. When that margin was non-positive, eta_start was clamped to log(1e-6), pinning the optimizer's start right on the variance-existence singularity (gama = 2a + 2), where the log-density has pathological gradients. ctp.fit() now falls back to a safe starting margin (with a warning()) instead of clamping to the boundary, and additionally tries a small grid of alternative starting points, keeping whichever converges to the best log-likelihood.

• Applied the same safe-margin fallback to the starting-value grid in zictp.fit(), which shared the same underlying clamp pattern in each of its multi-start candidates.

• ctp.fit() and zictp.fit() now flag fits whose estimated gama is very close to the variance-existence boundary (gama - 2*a - 2 near zero) with a warning(), a near_boundary element in the returned object, and a note in print()/summary() output -- instead of silently reporting Convergence: YES on an unreliable fit.

• Fixed a severe performance bug in dctp(): its normalization-tail loop grew a vector one element at a time via c(), which is O(n^2) in R. Near the gama = 2*a + 2 boundary the tail decays very slowly and can need close to the full 10000-term cap, making individual dctp() calls take seconds rather than milliseconds -- and this compounds badly across an optimizer's many function evaluations during ctp.fit()/ zictp.fit(), especially for the exact overdispersed, near-boundary data most likely to trigger the starting-value bug above. The tail buffer is now preallocated; a dctp() call near the boundary that previously took ~0.15s now takes ~0.002s, with identical numerical output.

zmctp 0.1.2

Bug fixes

• Fixed an incorrect mode formula in mode_ctp() and mode_zictp(). The previous implementation used a naive "round to nearest integer" rule that could return the wrong mode (e.g., returning 1 when the true mode was 0). Both functions now use the correct threshold-based rule.

• Fixed incorrect variance and skewness formulas in var_ctp() and skew_ctp(). The previous implementation used an incorrect algebraic combination of terms. var_zictp() and skew_zictp() required no changes and inherit the fix automatically, since they are built directly on var_ctp() and skew_ctp().

• Added an explicit validity check (gama > 0) to ctp.fit() and zictp.fit(). This closes a narrow edge case in the internal parameter reparameterization where sufficiently extreme starting conditions could otherwise allow an invalid (non-positive) gama during optimization. Typical fits to well-behaved data are unaffected.

Other changes

• tests/testthat/test-moments.R now contains a full suite of regression tests for mean_ctp(), var_ctp(), skew_ctp(), mode_ctp(), and their zero-modified counterparts, including a Monte Carlo cross-check of the skewness formula against simulated data from rzictp().

Note for existing users

If you used var_ctp(), skew_ctp(), var_zictp(), skew_zictp(), mode_ctp(), or mode_zictp() in version 0.1.1 or earlier, results from those functions will differ under 0.1.2 -- the new results are correct. We recommend re-running any analysis that relied on their numeric output.

zmctp 0.1.1

• Added exported functions for theoretical moments and mode of the CTP and Zero-Modified CTP distributions: mean_ctp(), var_ctp(), skew_ctp(), mode_ctp(), mean_zictp(), var_zictp(), skew_zictp(), mode_zictp(). These were previously computed only internally within summary.ctpfit() and summary.zictpfit().

zmctp 0.1.0

• Initial CRAN release.

zmctp documentation built on Sept. 20, 2026, 5:07 p.m.