The goal of blocking is to provide blocking methods for record linkage and deduplication using approximate nearest neighbour (ANN) algorithms and graph techniques. It supports multiple ANN implementations via rnndescent', RcppHNSW', RcppAnnoy', and mlpack packages, and provides integration with the reclin2 package. The package generates shingles from character strings and similarity vectors for record comparison, and includes evaluation metrics for assessing blocking performance including false positive rate (FPR) and false negative rate (FNR) estimates. For details see: Papadakis et al. (2020) <doi:10.1145/3377455>, Steorts et al. (2014) <doi:10.1007/978-3-319-11257-2_20>, Dasylva and Goussanou (2021) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X202100200002>, Dasylva and Goussanou (2022) <doi:10.1007/s42081-022-00153-3>.

Simulates age-at-onset traits associated with a segregating major gene in family data obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment correction is utilized to estimate age-dependent penetrance functions either parametrically from the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm can infer missing genotypes and carrier probabilities estimated from family's genotype and phenotype information or from a fitted model. Plot functions include pedigrees of simulated families and predicted penetrance curves based on specified parameter values. For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs, Journal of Statistical Software 97 (7), 1-30.

Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function).

This package implements the Stratigraphic Plug Alignment (SPA) procedure for integrating sparsely sampled plug-based measurements (e.g., total organic carbon, porosity, mineralogy) with high-resolution X-ray fluorescence (XRF) geochemical data. SPA uses linear interpolation via the base approx() function with constrained extrapolation (rule = 1) to preserve stratigraphic order and avoid estimation beyond observed depths. The method aligns all datasets to a common depth grid, enabling high-resolution multivariate analysis and stratigraphic interpretation of core-based datasets such as those from the Utica and Point Pleasant formations. See R Core Team (2025) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.html> and Omodolor (2025) <http://rave.ohiolink.edu/etdc/view?acc_num=case175262671767524> for methodological background and geological context.

Collision Risk Models for avian fauna (seabird and migratory birds) at offshore wind farms. The base deterministic model is derived from Band (2012) <https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>. This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code used here is heavily derived from this work with input from Dr A. Cook at the British Trust for Ornithology. These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, many of the species included in the stochastic collision risk models can also be found in the North Atlantic in the United States and Canada, and could be applied there.

This package implements functions that calculate upper prediction bounds on the false discovery proportion (FDP) in the list of discoveries returned by competition-based setups, implementing Ebadi et al. (2022) <arXiv:2302.11837>. Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression (note this package typically uses the terminology of TDC). Included is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the simultaneous standardized and uniform bands. Requires pre-computed Monte Carlo statistics available at <https://github.com/uni-Arya/fdpbandsdata>. This data can be downloaded by running the command devtools::install_github("uni-Arya/fdpbandsdata") in R and restarting R after installation. The size of this data is roughly 81Mb.

Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm by Tikka, Hyttinen and Karvanen (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) <doi:10.1609/aaai.v29i1.9679>, transportability (Bareinboim and Pearl, 2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, missing data (Mohan, Pearl, and Tian, 2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations of these. Also supports identification in the presence of context-specific independence (CSI) relations through labeled directed acyclic graphs (LDAG). For details on CSIs see (Corander et al., 2019) <doi:10.1016/j.apal.2019.04.004>.

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see <http://bioinformatica.mty.itesm.mx/difconet> for further information.

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.

Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. miselect presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) <doi:10.1080/10618600.2022.2035739>. They, by construction, force selection of the same variables across multiply imputed data. miselect also provides cross validated variants of these methods.

This package provides a comprehensive, simulation-based toolkit for power and sample-size analysis for linear and generalized linear mixed-effects models (LMMs and GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families via lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity grids; power curves and minimum sample-size solvers; parallel evaluation with deterministic seeds; and full reproducibility (manifests, result bundling, and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate, singular-fit rate, effective N) and publication-ready summary tables. References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using lme4" <doi:10.18637/jss.v067.i01>; Green and MacLeod (2016) "SIMR: an R package for power analysis of generalized linear mixed models by simulation" <doi:10.1111/2041-210X.12504>.

This package provides a method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.

Render R Markdown to Markdown (without using knitr), and Markdown to lightweight HTML or LaTeX documents with the commonmark package (instead of Pandoc). Some missing Markdown features in commonmark are also supported, such as raw HTML or LaTeX blocks, LaTeX math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all Pandoc Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and knitr. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and LaTeX). Book and website projects of multiple input documents are also supported.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsafvis R-package provides a collection of R-operators for the analysis and visualization of CM SAF NetCDF data. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).

Analyse light spectra for visual and non-visual (often called melanopic) needs, wrapped up in a Shiny App. Spectran allows for the import of spectra in various CSV forms but also provides a wide range of example spectra and even the creation of own spectral power distributions. The goal of the app is to provide easy access and a visual overview of the spectral calculations underlying common parameters used in the field. It is thus ideal for educational purposes or the creation of presentation ready graphs in lighting research and application. Spectran uses equations and action spectra described in CIE S026 (2018) <doi:10.25039/S026.2018>, DIN/TS 5031-100 (2021) <doi:10.31030/3287213>, and ISO/CIE 23539 (2023) <doi:10.25039/IS0.CIE.23539.2023>.

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 China, 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 China and Hong Kong, covering topics such as air quality, demographics, input-output tables, epidemiology, political structure, names, and social indicators. 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/>.

An approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.

Two implementations of canonical correlation analysis (CCA) that are based on iterated regression. By choosing the appropriate regression algorithm for each data domain, it is possible to enforce sparsity, non-negativity or other kinds of constraints on the projection vectors. Multiple canonical variables are computed sequentially using a generalized deflation scheme, where the additional correlation not explained by previous variables is maximized. nscancor() is used to analyze paired data from two domains, and has the same interface as cancor() from the stats package (plus some extra parameters). mcancor() is appropriate for analyzing data from three or more domains. See <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/> and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more details.

This package provides a unifying framework for managing and deploying shiny applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The shinymgr app builder "stitches" shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the shinymgr framework to write modules and seamlessly combine them into shiny apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination.

Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) <arXiv:1812:09384>.

Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions.