Weighted frequency and contingency tables of categorical variables and of the comparison of the mean value of a numerical variable by the levels of a factor, and methods to produce xtable objects of the tables and to plot them. There are also functions to facilitate the character encoding conversion of objects, to quickly convert fixed width files into csv ones, and to export a data.frame to a text file with the necessary R and SPSS codes to reread the data.

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.

Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>.

Accurate estimates of the diets of predators are required in many areas of ecology, but for many species current methods are imprecise, limited to the last meal, and often biased. The diversity of fatty acids and their patterns in organisms, coupled with the narrow limitations on their biosynthesis, properties of digestion in monogastric animals, and the prevalence of large storage reservoirs of lipid in many predators, led to the development of quantitative fatty acid signature analysis (QFASA) to study predator diets.

This package provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included. Most methods are described in Brockhoff, P.B. and Christensen, R.H.B. (2010) <doi:10.1016/j.foodqual.2009.04.003>.

Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* proposed by Berglund and Karlström (1999) <doi:10.1007/s101090050013>. The metric, an extended statistic of Getis/Ord G ('Getis and Ord 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, detects a group of flows having similar traits in terms of directionality. You provide OD data and the associated polygon to get results with several parameters, some of which are defined by spdep package.

Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. The estimating function allows flexible prior specification, specification of fixed parameters and several options for Markov Chain Monte Carlo posterior simulation. A basic version of the algorithm is described in: Cannas M. and Piras, N. (2025) <doi:10.1007/978-3-031-96303-2_53>.

This package wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. Conos focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes.

The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.

SGSeq is a package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.

Assists the evaluation of whether and where to focus code optimization, using Amdahl's law and visual aids based on line profiling. Amdahl's profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R's standard profiling tools and is not a wrapper for them.

This package provides functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.

This package provides a collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The bnRep_summary object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with bnRep_app() and is available online at <https://manueleleonelli.shinyapps.io/bnRep>. Reference: M. Leonelli (2025) <doi:10.1016/j.neucom.2025.129502>.

Given a non-linear model, calculate the local explanation. We purpose view the data space, explanation space, and model residuals as ensemble graphic interactive on a shiny application. After an observation of interest is identified, the normalized variable importance of the local explanation is used as a 1D projection basis. The support of the local explanation is then explored by changing the basis with the use of the radial tour <doi:10.32614/RJ-2020-027>; <doi:10.1080/10618600.1997.10474754>.

An easy-to-use yet powerful system for plotting grouped data effect sizes. Various types of effect size can be estimated, then plotted together with a representation of the original data. Select from many possible data representations (box plots, violin plots, raw data points etc.), and combine as desired. Durga plots are implemented in base R, so are compatible with base R methods for combining plots, such as layout()'. See Khan & McLean (2023) <doi:10.1101/2023.02.06.526960>.

Fits dose-response models using an evolutionary algorithm to estimate the model parameters. The procedure currently can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models as well as exponential models. Functions are also provided to plot, make predictions, and calculate confidence intervals for the resulting models. For details see "Nonlinear Dose-response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm", Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response 18(2):1559325820926734 (2020) <doi:10.1177/1559325820926734>.

Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.

We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2025) <doi:10.48550/arXiv.2507.09889>.

The n-vector framework uses the normal vector to the Earth ellipsoid (called n-vector) as a non-singular position representation that turns out to be very convenient for practical position calculations. The n-vector is simple to use and gives exact answers for all global positions, and all distances, for both ellipsoidal and spherical Earth models. This package is a translation of the Matlab library from FFI, the Norwegian Defence Research Establishment, as described in Gade (2010) <doi:10.1017/S0373463309990415>.

Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.

Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).

The Swash-Backwash Model for the Single Epidemic Wave was developed by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> to model the velocity of spread of infectious diseases across space. This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The package provides additional functions for bootstrap confidence intervals, country comparison, visualization of results, and data management. Furthermore, it contains several functions for analysis and visualization of (spatial) infection data.

Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) <doi:10.1021/acs.jctc.1c01163>. I/O functions for xtc format files were implemented using the xdrfile library available under open source license. The relevant information can be found in inst/COPYRIGHT.