Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. It utilizes the Frank-Wolfe algorithm to approximate so-called pivot measures: One-dimensional transport plans that fully describe the full transport, see G. Auricchio (2023) <doi:10.4171/RLM/1026>. For these, it offers methods for visualization and to extract the corresponding transport plans and costs. Additionally, related functions for one-dimensional optimal transport are available.

Calculates various chance-corrected agreement coefficients (CAC) among 2 or more raters are provided. Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. All of these statistical procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC.

This package performs multivariate meta-analysis for high-dimensional metabolomics data for integrating and collectively analysing individual-level data generated from multiple studies as well as for combining summary estimates. This approach accounts for correlation between outcomes, considers variability within and between studies, handles missing values and uses shrinkage estimation to allow for high dimensionality. A detailed vignette with example datasets and code to prepare data and analyses are available on <https://bookdown.org/a2delivera/MetaHD/>.

Estimation of marginal hazard ratios in clustered failure time data. It implements the weighted generalized estimating equation approach based on a semiparametric marginal proportional hazards model (See Niu, Y. Peng, Y.(2015). "A new estimating equation approach for marginal hazard ratio estimation"), accounting for within-cluster correlations. 5 different correlation structures are supported. The package is designed for researchers in biostatistics and epidemiology who require accurate and efficient estimation methods for survival analysis in clustered data settings.

This package provides tools for importing and cleaning Experience Sampling Method (ESM) data collected via the m-Path platform. The goal is to provide with a few utility functions to be able to read and perform some common operations in ESM data collected through the m-Path platform (<https://m-path.io/landing/>). Functions include raw data handling, format standardization, and basic data checks, as well as to calculate the response rate in data from ESM studies.

This package contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.

Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to Vii7 software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).

Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

Use behavioural variables to score activity and infer sleep from bouts of immobility. It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data. It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>), activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) and a new algorithm to detect when animals are dead.

Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.

This package provides a variational mapping approach that reveals and expands future temporal dynamics from folded high-dimensional geometric distance spaces, unfold turns a set of time series into a 4D block of pairwise distances between reframed windows, learns a variational mapper that maps those distances to the next reframed window, and produces horizon-wise predictive functions for each input series. In short: it unfolds the future path of each series from a folded geometric distance representation.

Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. See Happ et al. (2019, <doi:10.1002/sim.7983>) for details.

This package is an R implementation for fully unsupervised deconvolution of complex tissues. DebCAM provides basic functions to perform unsupervised deconvolution on mixture expression profiles by CAM and some auxiliary functions to help understand the subpopulation- specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures.

This package provides functions, data sets, analyses and examples from the third edition of the book A Handbook of Statistical Analyses Using R (Torsten Hothorn and Brian S. Everitt, Chapman & Hall/CRC, 2014). The first chapter of the book, which is entitled An Introduction to R, is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, Sweave source code for slides of selected chapters is included in this package.

An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers.

This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM.

Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.

This package provides simplified access to the data from the Catalog of Theses and Dissertations of the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES, <https://catalogodeteses.capes.gov.br>) for the years 1987 through 2022. The dataset includes variables such as Higher Education Institution (institution), Area of Concentration (area), Graduate Program Name (program_name), Type of Work (type), Language of Work (language), Author Identification (author), Abstract (abstract), Advisor Identification (advisor), Development Region (region), State (state).

This package provides a non-drawing graphic device for benchmarking purpose. In order to properly benchmark graphic drawing code it is necessary to factor out the device implementation itself so that results are not related to the specific graphics device used during benchmarking. The devoid package implements a graphic device that accepts all the required calls from R's graphic engine but performs no action. Apart from benchmarking it is unlikely that this device has any practical use.

This package provides a Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data. It estimates the number of clusters directly from the data using a Dirichlet-process prior. See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.

Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.

This package provides a population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.

Quantitative genetics tool supporting the modelling of multivariate genetic variance structures in quantitative data. It allows fitting different models through multivariate genetic-relationship-matrix (GRM) structural equation modelling (SEM) in unrelated individuals, using a maximum likelihood approach. Specifically, it combines genome-wide genotyping information, as captured by GRMs, with twin-research-based SEM techniques, St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>, Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.