Probabilistic Reconciliation via Conditioning

Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2024) , Bottom-Up Importance Sampling (Zambon et al., 2024) , methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) < https://proceedings.mlr.press/v244/zambon24a.html>, analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) .

Bayesian Reconciliation in the 'fable' Framework

Implements the 'bayesRecon' probabilistic reconciliation methods within the 'fable' framework for hierarchical time series forecasting. Bayesian reconciliation (bayesRecon) methods are accessed via the 'reconcile' verb, following 'fable' conventions. For methodological background, see Corani et al. (2021) , Zambon et al. (2024a) , Zambon et al. (2024b) < https://proceedings.mlr.press/v244/zambon24a.html>, and Carrara et al. (2025) .

Multistage Sampling Allocation and Sample Selection

Multivariate optimal allocation for different domains in one and two stages stratified sample design. 'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from < https://github.com/DiegoZardetto/ReGenesees>.

Article Formats for R Markdown

A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.

Parameter Estimation for the Averaging Model of Information Integration Theory

Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) < https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.

k-Nearest Neighbor Mutual Information Estimator

This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) , BC Ross (2014), and A. Tsimpiris (2012) , respectively.

Physics-Informed Spatial and Functional Data Analysis

An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

Life History Metrics from Matrix Population Models

Functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) .