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prLogistic - Estimation of Prevalence Ratios via Logistic Regression Models

Estimates adjusted prevalence ratios (PR) and their confidence intervals from logistic regression models, addressing the well-known limitation of odds ratios (OR) as approximations to PR in cross-sectional studies with common outcomes. Supports independent observations (glm()), clustered/multilevel data (glmer() from 'lme4'), longitudinal data via Generalised Estimating Equations (geeglm() from 'geepack'), and complex survey designs (svyglm() from 'survey'). Inference is available via the delta method (conditional and marginal standardisation) and via bootstrap (normal-approximation and percentile intervals). Continuous covariates are handled through user-specified or median-based reference values; flexible baseline specification allows any reference category to be chosen for factor predictors. Based on the methodology described in Amorim & Ospina (2021) <doi:10.1590/0001-3765202120190316>.

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5.03 score 12 scripts 330 downloads

logcumulant - Goodness-of-Fit Tests and Diagrams Based on Mellin Log-Cumulants

A family of three complementary goodness-of-fit tests based on an adaptation of Hotelling's T-squared statistic applied to vectors of sample log-cumulants (Mellin statistics) for positive-support reliability data. The package provides the asymptotic chi-squared reference and parametric bootstrap p-values for reliable finite-sample inference, covering the Weibull, Frechet, Gamma, Inverse-Gamma, Log-Normal, and Log-Logistic families. It also provides three diagnostic diagrams (log-cumulant, kurtosis-skewness, and coefficient-of-variation) with bootstrap concentration ellipses, in the spirit of moment-ratio diagrams. Methods are described in Santos, Ospina, Espinheira and Oliveira (2025).

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openblascpp

4.60 score 165 downloads

evbsreg - Local Influence Diagnostics for the Extreme-Value Birnbaum-Saunders Regression Model

Implements local influence diagnostics for the Extreme-Value Birnbaum-Saunders (EVBS) regression model: joint maximum likelihood estimation, conformal normal curvature diagnostics under three perturbation schemes (case-weight, response variable, and explanatory variable), randomized quantile residuals with simulation envelope, Monte Carlo simulation utilities, and publication-quality density and diagnostic plots. Version 1.1.0 adds the density, distribution and quantile functions, the finite upper endpoint, return levels and expected shortfall, block bootstrap standard errors for serially dependent series, local influence diagnostics for the generalized extreme-value regression model, and a GAMLSS family allowing the tail-shape parameter to depend on covariates. Version 1.2.0 adds a prospective control chart for endpoint identifiability. The methods are described in Ospina, Lima, Barros, and Macedo (2026, submitted) and are applied to monthly maximum wind gust data from Itajai, Brazil.

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4.40 score 432 downloads

simplexgof - Bootstrap-Calibrated Goodness-of-Fit Test for Simplex Regression

Implements the bootstrap-calibrated local-influence goodness-of-fit test for simplex regression models with constant or varying dispersion, following the local influence approach of Zhu and Zhang (2004) <doi:10.1093/biomet/91.3.579> and the simplex regression model of Barndorff-Nielsen and Jorgensen (1991) <doi:10.1016/0047-259X(91)90008-P>. The test statistic aggregates individual local-influence measures under case-weight perturbation. Because the first-order asymptotic normal calibration is severely liberal in finite samples, a parametric bootstrap calibration is provided that restores accurate size control and delivers high power against omitted covariates, neglected dispersion, and distributional misspecification. Plotting functions reproduce the figures and tables of the companion methodological paper. Computational kernels are implemented in 'C++' via 'Rcpp' and 'RcppArmadillo' for speed, and two real datasets are bundled.

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openblascpp

4.18 score 274 downloads

clis - Conformal Local Influence Screening for Bounded-Response Regression

Provides fast, statistically calibrated influence diagnostics for zero-or-one inflated beta (BIc) regression models with variable dispersion. The core idea is to use the conformal normal curvature of Poon and Poon (1999) <doi:10.1111/1467-9868.00162> as a non-conformity score within a split-conformal testing procedure, yielding per-observation conformal p-values whose Benjamini-Hochberg adjustment controls the false discovery rate at a user-specified level (Bates and others, 2023) <doi:10.1214/22-AOS2244>. Unlike classical local influence diagnostics, which rely on visual inspection of index plots and do not scale beyond a few hundred observations, 'clis' provides a finite-sample error guarantee and runs in linear time per observation after a single model fit. Methods for four perturbation schemes, block decomposition of influence into the inflation-probability and conditional-mean/precision components, penalised additive (semiparametric) submodels, and a full suite of diagnostic plots are included.

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3.70 score