Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation.

This package provides a unified Formula interface to the Synthetic Control Method (SCM) and related panel-data causal inference estimators: Synthetic Difference-in-Differences (SDID), Generalized Synthetic Control (GSC), Matrix Completion (MC), Time-Aware Synthetic Control (TASC), and Synthetic Interventions (SI), together with an experimental-design variant. Computational bottlenecks (quadratic programming, singular value decomposition, and Kalman filtering) are implemented in C++ via RcppArmadillo'. Methods are described in Abadie, Diamond and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746>, Arkhangelsky, Athey, Hirshberg, Imbens and Wager (2021) <doi:10.1257/aer.20190159>, Xu (2017) <doi:10.1017/pan.2016.2>, Athey, Bayati, Doudchenko, Imbens and Khosravi (2021) <doi:10.1080/01621459.2021.1891924>, and Agarwal, Shah and Shen (2025) <doi:10.1287/opre.2025.1590>.

This package provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) <doi:10.1016/0304-4076(77)90052-5> and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) <doi:10.1016/j.ijar.2015.06.001> and Maneejuk et al. (2017, ISBN:9783319562176).

This package provides access to ballot-level electoral microdata from the Florida 2000 general election and tools for computing summaries suitable for ecological inference. Includes functions to load data by county or race (election), compute marginal distributions at the precinct level, and build joint contingency arrays across multiple races for use with ecological inference packages. Data files are stored in a remote repository and downloaded on demand; local copies are supported via the data_dir option. Acknowledgements: We thank Jaime Ventura (ANES, University of Michigan) and Dan Keating (The Washington Post) for providing the raw data that serve as the starting point for the construction of this package. We also acknowledge funding from the Conselleria de Educación, Cultura y Universidades (grant CIACIO/2023/031).

We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faithâ s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. All functions rely on construction of a core community phylogeny, which is a phylogeny where branches are defined based on their presence in multiple samples from a single type of habitat. Our package provides two options for constructing the core community phylogeny, a tip-based approach, where the core community phylogeny is identified based on incidence of leaf nodes and a branch-based approach, where the core community phylogeny is identified based on incidence of individual branches. We suggest use of the microViz package.

We develop a novel matrix factorization tool named scINSIGHT to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, scINSIGHT simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.

CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus.

This package provides a lightweight interface to graphics processing unit (GPU)-accelerated numerical computing using CUDA'. Dense tensors, sparse matrices, decompositions, distances, exact nearest neighbours, clustering, graph workflows, and embeddings share one consistent interface. The native backend discovers the NVIDIA CUDA Driver API', cuBLAS', and cuSOLVER libraries at runtime without bundling LibTorch or the CUDA Runtime'. Stage-level provenance records the backend, device, and data transfers used by each result. A portable implementation supports package validation on systems without CUDA'. Background for the included Leiden community detection and uniform manifold approximation and projection methods is given by Traag, Waltman and van Eck (2019) <doi:10.1038/s41598-019-41695-z> and McInnes et al. (2018) <doi:10.21105/joss.00861>, respectively.

This package provides geometric methods for nonparametric regression and density estimation on data represented as coordinate matrices or weighted graphs. Methods include local polynomial smoothing, model-averaged local polynomial smoothing, local polynomial lifting trend filtering, synchronized local polynomial lifting trend filtering, graph low-pass filtering, and Hessian-energy regression. Methodological references include Gajer and Ravel (2025) "Adaptive Geometric Regression for High-Dimensional Structured Data" <doi:10.48550/arXiv.2511.03817>, Fan and Gijbels (1996, ISBN:9780412983214), Wang et al. (2016) "Trend Filtering on Graphs" <https://www.jmlr.org/papers/v17/15-147.html>, and Kim et al. (2009) "Semi-Supervised Regression Using Hessian Energy" <https://papers.nips.cc/paper/3741-semi-supervised-regression-using-hessian-energy-with-an-application-to-semi-supervised-dimensionality-reduction>.

This package provides functions to access data from public RESTful APIs including Nager.Date', World Bank API', and REST Countries API', retrieving real-time or historical data related to Japan, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on Japan, covering topics such as natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.

Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in multimark combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, multimark also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.

This package provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the nimble package. This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers. The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>. The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling. Functionality of the nimbleHMC package is described further in Turek, et al (2024) <doi: 10.21105/joss.06745>.

This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.

Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <doi:10.48550/arXiv.2008.05693>.

This package provides a pair of Shiny modules that let end users of a Shiny application author their own R functions directly in the browser. Host apps can expose these modules as extension points where user-supplied code augments or replaces built-in logic, without requiring users to modify the app's source. Each module embeds an Ace editor with a structured argument table, an in-frame R console rooted in the paused function's local environment, and a step debugger that handles for, while, repeat, and if/else blocks at any nesting depth. Two module flavours are provided: solo editors for testing a function in isolation with literal argument values, and embedded editors for pausing a function mid-execution inside a larger host program.

Implementation of Bayesian and frequentist Weibull Shape Parameter (WSP) tests for signal detection in pharmacovigilance based on right-censored time-to-event data to flag associations between drugs and adverse events. The WSP test is based on the assumption of constant hazard reflected by a Weibull type distribution with shape parameters equal to one. Based on the shape parameter estimates (posterior distribution or point estimate), the WSP test method performs a hypothesis test on each shape parameter and combines them to a decision on the presence of a signal. Methods described in Sauzet and Cornelius (2022) <doi:10.3389/fphar.2022.889088>, Sauzet et al. (2024) <doi:10.1007/s40264-024-01460-2>, and Dyck and Sauzet (2025) <doi:10.48550/arXiv.2412.05463>.

This package implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. The methodology is based on a model in which each component has a fluctuating mean represented by a random walk with occasional abrupt shifts, combined with a stationary vector autoregressive structure to capture temporal and cross-sectional dependence. The framework is broadly applicable to correlated multivariate sequences in which large, sudden shifts occur in all or subsets of components and are the primary targets of interest, whereas small, smooth fluctuations are not. Although random walks are used as a modeling device, they provide a flexible approximation for a wide class of slowly varying or locally smooth dynamics, enabling robust performance beyond the strict random walk setting.

This package provides estimation and leave-one-cluster-out jackknife standard errors for four longitudinal cluster-randomized trial estimands: horizontal individual average treatment effect (h-iATE), horizontal cluster average treatment effect (h-cATE), vertical individual average treatment effect (v-iATE), and vertical cluster-period average treatment effect (v-cATE), using unadjusted and augmented (model-robust standardization) estimators. The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) <doi:10.48550/arXiv.2507.17190>.

The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.

This package provides tools for the analysis of multi-environment agronomic trials, with a specific focus on plant breeding experiments. Implements the Additive Main effects and Multiplicative Interaction (AMMI) model (Gauch, 1992, ISBN:9780444892409) and the Site Regression (SREG) model (Cornelius, 1996, <doi:10.1201/9780367802226>). To ensure reliable results even with outliers or missing data, it includes robust versions of AMMI (Rodrigues et al., 2016, <doi:10.1093/bioinformatics/btv533>) and SREG (Angelini et al., 2022, <doi:10.1080/15427528.2022.2051217>). Furthermore, the package offers advanced imputation techniques for multi-environment data, covering classical methodologies (Arciniegas-Alarcón et al., 2014, <doi:10.2478/bile-2014-0006>) and recently published imputation methods for MET data (Angelini et al., 2024, <doi:10.1007/s10681-024-03344-z>).

This package provides a collection of parametric and nonparametric methods for the analysis of survival data, described in <doi:10.32614/RJ-2025-034>. Parametric families implemented include Gompertz-Makeham, exponential and generalized Pareto models and extended models. The package includes an implementation of the nonparametric maximum likelihood estimator for arbitrary truncation and censoring pattern based on Turnbull (1976) <doi:10.1111/j.2517-6161.1976.tb01597.x>, along with graphical goodness-of-fit diagnostics. Parametric models for positive random variables and peaks over threshold models based on extreme value theory are described in Rootzén and Zholud (2017) <doi:10.1007/s10687-017-0305-5>; Belzile et al. (2021) <doi:10.1098/rsos.202097> and Belzile et al. (2022) <doi:10.1146/annurev-statistics-040120-025426>.

The biomarker data set by Vermeulen et al. (2009) <doi:10.1016/S1470-2045(09)70154-8> is provided. The data source, however, is by Ruijter et al. (2013) <doi:10.1016/j.ymeth.2012.08.011>. The original data set may be downloaded from <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>. This data set is for a real-time quantitative polymerase chain reaction (PCR) experiment that comprises the raw fluorescence data of 24,576 amplification curves. This data set comprises 59 genes of interest and 5 reference genes. Each gene was assessed on 366 neuroblastoma complementary DNA (cDNA) samples and on 18 standard dilution series samples (10-fold 5-point dilution series x 3 replicates + no template controls (NTC) x 3 replicates).

Analyze and compare conversations using various similarity measures including topic, lexical, semantic, structural, stylistic, sentiment, participant, and timing similarities. Supports both pairwise conversation comparisons and analysis of multiple dyads. Methods are based on established research: Topic modeling: Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>; Landauer et al. (1998) <doi:10.1080/01638539809545028>; Lexical similarity: Jaccard (1912) <doi:10.1111/j.1469-8137.1912.tb05611.x>; Semantic similarity: Salton & Buckley (1988) <doi:10.1016/0306-4573(88)90021-0>; Mikolov et al. (2013) <doi:10.48550/arXiv.1301.3781>; Pennington et al. (2014) <doi:10.3115/v1/D14-1162>; Structural and stylistic analysis: Graesser et al. (2004) <doi:10.1075/target.21131.ryu>; Sentiment analysis: Rinker (2019) <https://github.com/trinker/sentimentr>.