We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.

The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach.

This package provides transparent, contract-first infrastructure for Bayesian analysis of repeated-measures and hierarchical behavioural data. It supports approved Bernoulli-logit, positive lognormal duration, and governed Gaussian dynamic-pupillometry workflows with strict readiness auditing, deterministic simulation, explicit preparation and transformation replay, inspectable scale-aware priors, prior and posterior predictive checks, restricted optional fitting through brms with either rstan or cmdstanr', sampling and temporal diagnostics, explicit posterior estimands, sensitivity analysis, target-specific predictive validation, simulation-based calibration, and conservative reporting. Core contracts and validation remain backend-independent. Version 0.5 adds governed robust and distributional dynamic pupillometry, bounded ARMA residual structures, Gaussian-process trajectories, explicit measurement uncertainty and missing-data models, joint binocular analysis, predictive model comparison, functional posterior estimands, and experimental nonlinear response-shape models while preserving explicit scientific and computational governance boundaries.

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>).

This package provides statistical methods for analytical method comparison and validation studies. Implements Bland-Altman analysis for assessing agreement between measurement methods (Bland & Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>), Passing-Bablok regression for non-parametric method comparison (Passing & Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>), and Deming regression accounting for measurement error in both variables (Linnet (1993) <doi:10.1093/clinchem/39.3.424>). Also includes tools for setting quality goals based on biological variation (Fraser & Petersen (1993) <doi:10.1093/clinchem/39.7.1447>) and calculating Six Sigma metrics, precision experiments with variance component analysis, precision profiles for functional sensitivity estimation (Kroll & Emancipator (1993) <https://pubmed.ncbi.nlm.nih.gov/8448849/>). Commonly used in clinical laboratory method validation. Provides publication-ready plots and comprehensive statistical summaries.

This package provides additional data sets, methods and documentation to complement the vcd package for Visualizing Categorical Data and the gnm package for Generalized Nonlinear Models. In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and adds a 3D version in mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of glm and loglm objects. This package is now a support package for the book Friendly, M. and Meyer, D. (2016, ISBN:978-1-4987-2583-5) Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data'. Recent work adds colorized tables of frequencies to highlight patterns of association, association graphs to visualize conditional independence and a variety of new or improved statistical tests for categorical data analysis.

This package provides a universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected codedittoColors().

DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.

Tool for demographic modeling and analysis, combining stochastic simulation-based projections with classic demographic methods. Provides utilities for disaggregating population data using Karup-King interpolation, fitting Brass relational logit models, calculating fertility and mortality metrics, building complete and abridged life tables with model-based separation factors, estimating child mortality indirectly from children ever born and surviving (Brass-Trussell method), decomposing and standardizing rates, and performing hierarchical subnational population projections. Relational life table estimation and interpolation methods are described in Brass (1975) "Methods for Estimating Fertility and Mortality from Limited and Defective Data", Preston et al. (2001, ISBN:978-0631226161) "Demography: Measuring and Modeling Population Processes", Siegel and Swanson (2004, ISBN:978-0126419559) "The Methods and Materials of Demography", and Raftery et al. (2012) "Bayesian probabilistic population projections for all countries" <doi:10.1073/pnas.1211452109>.

This package provides a method to detect values poorly explained by a Gaussian linear model. The procedure is based on the maximum of the absolute value of the studentized residuals, which is a parameter-free statistic. This approach generalizes several procedures used to detect abnormal values during longitudinal monitoring of biological markers. Methodological details are provided in Berthelot G., Saulière G., and Dedecker J. (2025), "DEViaN-LM An R Package for Detecting Abnormal Values in the Gaussian Linear Model", HAL Id: hal-05230549, <https://hal.science/hal-05230549>, and in Berthelot G., Gelein B., Meinadier E., Orhant E., and Dedecker J. (2026), "A guide to z-score-based methods, with illustrations from biological data sets", The Journal of Sport and Exercise Science 10, 54â 70, <doi:10.36905/jses.2026.01.06>.

This package provides functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2021): <https://arfjournals.com/image/catalog/Journals%20Papers/AJSS/No%202%20(2021)/4-AJSS_123-150.pdf>; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140.

This package implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.

Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.

Input a list of words and/or their phonological transcriptions and this package creates a language network based on their neighborhood structure. First, the phonological/orthographic neighbors for each item in the list are identified based on various definitions of a neighbor (e.g., edit-distance (substitution, deletion, or addition), substitution-only; distance size (1-edit or more); based on single characters or segments indicated by separators) and summarizes this information in an igraph network object for subsequent analyses. For more details see Luce & Pisoni (1998) <doi:10.1097/00003446-199802000-00001> and Vitevitch (2008) <doi:10.1044/1092-4388(2008/030)>. Helper functions for extracting network metrics, neighbors, and other information from the language network are provided. This package is intended for psycholinguists interested in modeling language networks and word neighborhoods in various languages.

Provides an interactive toolkit for educational and psychological measurement implemented using the shiny framework. The package supports content validity analysis, dimensionality assessment, and Classical Test Theory using the CTT package (Willse, 2018) <doi:10.32614/CRAN.package.CTT>. Item Response Theory (IRT) analyses are conducted via mirt (Chalmers, 2012) <doi:10.18637/jss.v048.i06>. Exploratory Factor Analysis is performed using psych (Revelle, 2025), while Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) are based on the lavaan framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. The CFA/SEM module features interactive model specification, automatic model comparison, modification indices, comprehensive fit diagnostics, path diagram visualization, and HTML report generation. The application allows users to upload data, evaluate statistical models, visualize results, and export outputs through an intuitive graphical interface without requiring programming experience.

This package provides mathematical tools for simulating and visualizing three-dimensional octahedral quantum wave interferences and spatial resonance fields. Includes functions for MRI slice generation of fullerene structures and wave models. Computational modeling and three-dimensional visualization of fullerene and octahedral topologies are implemented within the R statistical environment, with interactive plotting powered by plotly'. Theoretical foundations are based on the topological frameworks of Cataldo et al. (2015) <doi:10.1002/wcms.1207>, Dresselhaus et al. (1996, ISBN:9780122218200), and Coxeter (1973, ISBN:9780486614809); the geometric principles of equations of the octahedron type are outlined in Bobenko and Suris (2012) <doi:10.1093/imrn/rnr083>. Additional structural and biological symmetry contexts are derived from Bragg (1914) <doi:10.1098/rspa.1914.0015> and Caspar and Klug (1962) <doi:10.1101/sqb.1962.027.001.005>.

Testing and documenting code that communicates with remote servers can be painful. Dealing with authentication, server state, and other complications can make testing seem too costly to bother with. But it doesn't need to be that hard. This package enables one to test all of the logic on the R sides of the API in your package without requiring access to the remote service. Importantly, it provides three contexts that mock the network connection in different ways, as well as testing functions to assert that HTTP requests were---or were not---made. It also allows one to safely record real API responses to use as test fixtures. The ability to save responses and load them offline also enables one to write vignettes and other dynamic documents that can be distributed without access to a live server.

Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The APCtools package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.

This package provides a complete workflow for exploratory factor analysis (EFA). It covers data screening and factorability checks, a suite of factor retention criteria for choosing the number of factors, and factor extraction by principal axis factoring, maximum likelihood, unweighted least squares, or diagonally weighted least squares from Pearson, Spearman, Kendall, polychoric, tetrachoric, or two-stage full-information maximum likelihood correlations. A built-in rotation engine offers a range of orthogonal and oblique rotations, and standard errors for loadings and related quantities can be obtained by analytic, robust, or bootstrap methods. Further tools support model averaging across analytic choices, multigroup EFA with factor congruence, EFA on multiply imputed data, Schmid-Leiman transformation, reliability coefficients (including McDonald's omegas), factor score estimation, data simulation, and power analysis. Computationally intensive procedures are implemented in C++ for speed.

This package provides a collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as compositions', robCompositions', sp', gstat and RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.

Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. iNEXT.3D extends iNEXT to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) <doi:10.1111/2041-210X.13682> for more details.

This package implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, with a focus on multivariate repeated measures and functional data. The package provides two approaches: (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional representations for exploratory analysis and visualization of dependence structures across multiple feature sets. The implementation follows the framework developed in Górecki, KrzyÅ ko, Gnettner and Kokoszka (2025) <doi:10.48550/arXiv.2510.04457>.

This Haskell package is intended for those who are tired of keeping long lists of dependencies to the same essential libraries in each package as well as the endless imports of the same APIs all over again.

It also supports the modern tendencies in the language.

To solve those problems this package does the following:

• Reexport the original APIs under the Rebase namespace.

• Export all the possible non-conflicting symbols from the Rebase.Prelude module.

• Give priority to the modern practices in the conflicting cases.

The policy behind the package is only to reexport the non-ambiguous and non-controversial APIs, which the community has obviously settled on. The package is intended to rapidly evolve with the contribution from the community, with the missing features being added with pull-requests.

The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) <doi:10.1080/14686996.2019.1620123>, <doi:10.1080/27660400.2021.1899449> <doi:10.1080/27660400.2022.2159753>. The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.