This package provides utility functions to deal with Italian fiscal code ('codice fiscale').

This package provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code.

For environmental chemists, ecologists, researchers and agricultural scientists to understand the dissipation kinetics, calculate the half-life periods and rate constants of compounds, pesticides, contaminants in different matrices.

It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" <doi:10.1007/s00180-024-01536-8>.

Generate plots based on the Item Pool Visualization concept for latent constructs. Item Pool Visualizations are used to display the conceptual structure of a set of items (self-report or psychometric). Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., Connection Weights described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like SmoothGrad described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, Gradient x Input or Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

Converts character vectors between phonetic representations. Supports IPA (International Phonetic Alphabet), X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and ARPABET (used by the CMU Pronouncing Dictionary).

Compute onestep and multistep time series forecasts for machine learning models.

Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the INLA package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).

Converts matrices and lists of matrices into a single vector by interleaving their values. That is, each element of the result vector is filled from the input matrices one row at a time. This is the same as transposing a matrix, then removing the dimension attribute, but is designed to operate on matrices in nested list structures.

This package implements multiple variants of the Information Bottleneck ('IB') method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. The package provides deterministic, agglomerative, generalized, and standard IB clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2024) <doi:10.48550/arXiv.2407.03389>. The standard IB method originates from Tishby et al. (2000) <doi:10.48550/arXiv.physics/0004057>, the agglomerative variant from Slonim and Tishby (1999) <https://papers.nips.cc/paper/1651-agglomerative-information-bottleneck>, and the generalized IB from Strouse and Schwab (2017) <doi:10.1162/NECO_a_00961>.

This package provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro Calmar for SAS, so that Calmar users can quickly get used to icarus'. In addition to calibration (with linear, raking and logit methods), icarus features functions for calibration on tight bounds and penalized calibration.

This package implements an S7 class for estimates based on influence functions, with forward mode automatic differentiation defined for standard arithmetic operations.

This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.

Interactive shiny application for running Item Response Theory analysis. Provides graphics for characteristic and information curves.

Convenient functions to create ggplot2 graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).

This package provides a framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers.

Models, analyzes, and forecasts financial intraday signals. This package currently supports a univariate state-space model for intraday trading volume provided by Chen (2016) <doi:10.2139/ssrn.3101695>.

Download ifo business survey data and more time series from ifo institute <https://www.ifo.de/en/ifo-time-series>.

This package provides functions to to compute a continuum of information-based measures for quantifying the temporal stability of populations, communities, and ecosystems, as well as their associated synchrony, based on species (or species assemblage) biomass or other key variables. When biodiversity data are available, the package also enables the assessment of the corresponding diversityâ stability relationships. All measures are applicable in both temporal and spatial contexts. The theoretical and methodological background is detailed in Chao et al. (2025) <doi:10.1101/2025.08.20.671203>.

Categorization and scoring of injury severity typically involves trained personnel with access to injured persons or their medical records. icdpicr contains a function that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS') from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury severity scoring. ICDPIC was originally developed in Stata', and icdpicr is an open-access update that accepts both ICD-9 and ICD-10 codes.

This package provides tools for manipulating, visualizing, and exporting raster images in R. Designed as an educational resource for students learning the basics of remote sensing, the package provides user-friendly functions to apply color ramps, export RGB composites, and create multi-frame visualizations. Built on top of the terra and ggplot2 packages. See <https://github.com/ducciorocchini/imageRy> for more details and examples.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

This package performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the hdi package.