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 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>.

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>.

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 Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with Stan through rstan or cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) <doi:10.18637/jss.v076.i01> and Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.

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>.

This package provides functions for estimation under the Randomised Response Technique for sensitive survey data, including Warner's estimator, Optional Randomised Response Technique estimator proposed by Chaudhuri and Mukerjee,and the Optimized Optional Randomised Response Technique estimator proposed by Pushadapu et al. The package also includes Monte Carlo simulation tools for evaluating estimator performance. The implemented methods are based on Warner (1965) <doi:10.1080/01621459.1965.10480775>, Chaudhuri and Mukerjee (1985),and Pushadapu et al. (2025) <doi: 10.1111/insr.12581>.

This package provides functions that automate accessing, downloading and exploring Soil Moisture and Ocean Salinity (SMOS) Level 4 (L4) data developed by Barcelona Expert Center (BEC). Particularly, it includes functions to search for, acquire, extract, and plot BEC-SMOS L4 soil moisture data downscaled to ~1 km spatial resolution. Note that SMOS is one of Earth Explorer Opportunity missions by the European Space Agency (ESA). More information about SMOS products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.

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.

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.

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.

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 smurf package contains the implementation of the Sparse Multi-type Regularized Feature (SMuRF) modeling algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:

• Selection of the regularization tuning parameter lambda using three different approaches: in-sample, out-of-sample or using cross-validation.

• S3 methods to handle the fitted object including visualization of the coefficients and a model summary.

dplyr is the next iteration of plyr. It is focused on tools for working with data frames. It has three main goals: 1) identify the most important data manipulation tools needed for data analysis and make them easy to use in R; 2) provide fast performance for in-memory data by writing key pieces of code in C++; 3) use the same code interface to work with data no matter where it is stored, whether in a data frame, a data table or database.

This package implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are readr wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.

GOfan provides an intuitive and compact visualization of Gene Ontology (GO) enrichment results using a sunburst layout inspired by SynGO, preserving hierarchical relationships among GO terms and allowing color-based encoding of information such as p-values or gene counts. By converting complex GO DAGs into clean, circular representations, it allows researchers to quickly grasp the hierarchical structure and biological significance of enriched terms. The interactive and customizable visualizations facilitate exploration of key GO categories, enhancing interpretation and presentation of enrichment analyses.

Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.

This package provides a comprehensive set of functions designed for multivariate mean monitoring using the Critical-to-X Control Chart. These functions enable the determination of optimal control limits based on a specified in-control Average Run Length (ARL), the calculation of out-of-control ARL for a given control limit, and post-signal analysis to identify the specific variable responsible for a detected shift in the mean. This suite of tools provides robust support for precise and effective process monitoring and analysis.

This package implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) <doi:10.1016/j.jeconom.2022.12.011>, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by SÅ oczyÅ ski (2024) <doi:10.48550/arXiv.2011.06695> as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) <doi:10.1080/01621459.1995.10476535>.