Estimation and bootstrap utilities for single layer and multilayer Mixed Graphical Models, including functions for centrality, bridge metrics, membership stability, and plotting (De Martino et al. (2026) <doi:10.48550/arXiv.2602.05716>).

Focus-glue-context (FGC) fisheye transformations to two-dimensional coordinates and spatial vector geometries. Implements a smooth radial distortion that enlarges a focal region, transitions through a glue ring, and preserves outside context. Methods build on generalized fisheye views and focus+context mapping. For more details see Furnas (1986) <doi:10.1145/22339.22342>, Furnas (2006) <doi:10.1145/1124772.1124921> and Yamamoto et al. (2009) <doi:10.1145/1653771.1653788>.

This package provides an R wrapper for the MD4C (Markdown for C') library. Functions exist for parsing markdown ('CommonMark compliant) along with support for other common markdown extensions (e.g. GitHub flavored markdown, LaTeX equation support, etc.). The package also provides a number of higher level functions for exploring and manipulating markdown abstract syntax trees as well as translating and displaying the documents.

Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package meteoEVT provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, <doi:10.1111/j.1600-0870.2007.00272.x>], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, <doi:10.1017/S1350482798000553>], the F diagnostic [Parfitt et al., 2017, <doi:10.1002/2017GL073662>] and the DSI [Mack et al., 2022, <arXiv:2208.11438>].

Converts results from the manymome package, presented in Cheung and Cheung (2023) <doi:10.3758/s13428-023-02224-z>, to publication-ready tables.

The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

Implementation of custom tidymodels metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problemsâ <doi:10.1016/j.knosys.2020.106490> and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) â Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificityâ <doi:10.3760/cma.j.issn.0366-6999.20123102>.

This package provides a collection of functions for converting and visualization the free induction decay of mono dimensional nuclear magnetic resonance (NMR) spectra into an audio file. It facilitates the conversion of Bruker datasets in files WAV. The sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The package includes also NMR spectra of the urine samples provided by four healthy donors. Based on Cacciatore S, Saccenti E, Piccioli M. Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments. OMICS. 2015;19(3):147-56. <doi:10.1089/omi.2014.0131>.

Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

Create variable width bar charts i.e. "bar mekko" charts to include important quantitative context. Closely related to mosaic, spine (or spinogram), matrix, submarine, olympic, Mondrian or product plots and tree maps.

Constructs genetic linkage maps in autopolyploid full-sib populations. Uses pairwise recombination fraction estimation as the first source of information to sequentially position allelic variants in specific homologous chromosomes. For situations where pairwise analysis has limited power, the algorithm relies on the multilocus likelihood obtained through a hidden Markov model (HMM). Methods are described in Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378> and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.

Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) <doi:10.3758/s13428-020-01409-0>.

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

This package produces clean and neat Markdown log file and also provide an argument to include the function call inside the Markdown log.

This package provides a set of tools to perform multiple versions of the Mobility Oriented-Parity metric. This multivariate analysis helps to characterize levels of dissimilarity between a set of conditions of reference and another set of conditions of interest. If predictive models are transferred to conditions different from those over which models were calibrated (trained), this metric helps to identify transfer conditions that differ substantially from those of calibration. These tools are implemented following principles proposed in Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, and expanded to obtain more detailed results that aid in interpretation as in Cobos et al. (2024) <doi:10.21425/fob.17.132916>.

Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) <doi:10.1101/gr.262675.120> and Chen et al. (2022) <doi:10.1186/s13059-022-02627-9> for associated statistical details.

An implementation of a taxonomy of models of restricted diffusion in biological tissues parametrized by the tissue geometry (axis, diameter, density, etc.). This is primarily used in the context of diffusion magnetic resonance (MR) imaging to model the MR signal attenuation in the presence of diffusion gradients. The goal is to provide tools to simulate the MR signal attenuation predicted by these models under different experimental conditions. The package feeds a companion shiny app available at <https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical interface to the models and tools provided by the package. Models currently available are the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van Gelderen et al. (1994) <doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997) <doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995) <doi:10.1006/jmra.1995.0014> and Callaghan (1995) <doi:10.1006/jmra.1995.1055>.

An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008) <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>, for R. This package builds on the original Matlab and C++ implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in.

This package provides a set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440.

Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).

This package provides a collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.

An ensemble meta-prediction framework to integrate multiple regression models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) <arXiv:2010.09971>. A meta-analysis framework along with two weighted estimators as the ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators.

This package provides functions similar to the SAS macros previously provided to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826> and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers which outline practical benefits and challenges of factorial and fractional factorial experiments for scientists interested in developing biological and/or behavioral interventions, especially in the context of the multiphase optimization strategy (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>. The package currently contains three functions. First, RelativeCosts1() draws a graph of the relative cost of complete and reduced factorial designs versus other alternatives. Second, RandomAssignmentGenerator() returns a dataframe which contains a list of random numbers that can be used to conveniently assign participants to conditions in an experiment with many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect size, or required sample size of a factorial or fractional factorial experiment, for main effects or interactions, given several possible choices of effect size metric, and allowing pretests and clustering.