Taxonomic Hierarchy Distances and Lineage Analysis

Computes distances between taxonomic hierarchy nodes using lineage data retrieved from The Taxonomicon < http://taxonomicon.taxonomy.nl>. For distinct nodes, distance is defined as the reciprocal of the depth of their most recent common ancestor; identical nodes have distance zero. This definition yields an ultrametric within each connected hierarchy. Functions are provided for auditable name resolution, online or user-supplied lineage analysis, clade membership, pairwise and matrix distance calculation, hierarchical clustering, principal coordinates analysis, portable JSON analysis bundles, and cache management. Distance matrices are returned as base R 'dist' objects. The distances represent classification depth rather than evolutionary time or phylogenetic branch length.

Regularized Point Processes and Stochastic Marginalization for Extremes

Implements a non-stationary extreme value analysis framework by coupling a covariate-driven Non-Homogeneous Poisson Process (NHPP) with Elastic-Net regularization and analytical gradients. Provides methods for estimating conditional return levels and unconditional (marginalized) return levels via parametric stochastic integration over stable Vector Autoregressive VAR(p) or univariate autoregressive covariate trajectories, or non-parametric annual-block resampling. Supports block-specific penalty controls, operational active-set thresholds, conditional parametric bootstrap inference, and walk-forward assessment.

Pathways Longitudinal and Differential Analysis in Metabolomics

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method, described in Guilmineau et al (2025) is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

Weighted Cox-Regression for Nested Case-Control Data

Fit Cox proportional hazard models with a weighted partial likelihood. It handles one or multiple endpoints, additional matching and makes it possible to reuse controls for other endpoints Stoer NC and Samuelsen SO (2016) .

Inverse Probability of Censoring Weights to Deal with Treatment Switch in Randomized Clinical Trials

Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials.

HIC diffeREntial Analysis Method

Perform Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

Remote Sensing Metrics for Spatial Health Analysis

Calculate and extract remote sensing metrics for spatial analysis in the field of health. The package offers R users a quick and straightforward way to obtain areal or zonal statistics of key environmental indicators, covariates, and vector-borne disease data ideal for modeling infectious diseases within the framework of spatial epidemiology.

Propensity Score Predictive Inference for Generalizability and Transportability

Provides a suite of Propensity Score Predictive Inference (PSPI) methods to generalize treatment effects in trials to target populations. The package includes an existing model Bayesian Causal Forest (BCF) and four PSPI models (BCF-PS, FullBART, SplineBART, DSplineBART). These methods leverage Bayesian Additive Regression Trees (BART) to adjust for high-dimensional covariates and nonlinear associations, while SplineBART and DSplineBART further use propensity score based splines to address covariate shift between trial data and target population.

Inference About the Standardized Mortality Ratio when Evaluating the Effect of a Screening Program on Survival

Functions to make inference about the standardized mortality ratio (SMR) when evaluating the effect of a screening program. The package is based on methods described in Sasieni (2003) and Talbot et al. (2011) .

Excess Hazard Modelling Considering Inappropriate Mortality Rates

Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) . This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) .