Despite the recent development of methods dealing with partially observed epidemics, some limitations remain in practice, mainly related to the amount of augmented data and the adjustment of numerous tuning parameters. Our aim is to propose a generic inference method easily practicable and able to deal with discrete, incomplete and noisy outbreak d...
Frequently, population studies feature pyramidally-organized data represented using Hierarchical Bayesian Models (HBM) enriched with plates. These models can become prohibitively large in settings such as neuroimaging, where a sample is composed of a functional MRI signal measured on 64 thousand brain locations, across 4 measurement sessions, and a...
International audience
This work is motivated by the study of local protein structure, which is defined by two variable dihedral angles that take values from probability distributions on the flat torus. Our goal is to provide the space $\mathcal{P}(\mathbb{R}^2/\mathbb{Z}^2)$ with a metric that quantifies local structural modifications due to changes in the protein seque...
We propose a computationally efficient procedure for elevated mean detection on a connected subgraph of a network with node-related scalar observations. Our approach relies on two intuitions: first, a significant concentration of high observations in a connected subgraph implies that the subgraph induced by the nodes associated with the highest obs...
Monitoring the Covid19 pandemic is critical to design sanitary policies. Recently, reliable estimates of the pandemic reproduction number were obtained from a nonsmooth convex optimization procedure designed to fit epidemiology requirements and to be robust to the low quality of the data (outliers, pseudoseasonalities,. . .). Applied to daily new i...
Variable selection methods are widely used in molecular biology to detect biomarkers or to infer gene regulatory networks from transcriptomic data. Methods are mainly based on the high-dimensional Gaussian linear regression model and we focus on this framework for this review. We propose a comparison study of variable selection procedures from regu...
Regulatory bodies requires evaluation of bioaccumulation of chemicals within organisms with the objective to better assess risks linked to the toxicity of active substances. To this end, toxicokinetic (TK) data are particularly useful to relate the chemical exposure concentration to the accumulation and depuration processes happening within organis...
Package morse is devoted to the analysis of experimental data collected from standard toxicity tests. It provides ready-to-use functions to visualize a data set and to estimate several toxicity indices to be further used in support of environmental risk assessment in full compliance with regulatory requirements. Such toxicity indices are indeed cla...
We present the winning strategy of an electricity demand forecasting competition. This competition was organized to design new forecasting methods for unstable periods such as the one starting in Spring 2020. We rely on state-space models to adapt standard statistical and machine learning models. We claim that it achieves the right compromise betwe...
