The increasing popularity of geographically weighted (GW) techniques has resulted in the development of several R packages, such as GWmodel'. To facilitate their usages, GWmodelVis provides a shiny'-based interactive visualization toolkit for geographically weighted (GW) models. It includes a number of visualization tools, including dynamic mapping of parameter surfaces, statistical visualization, sonification and exporting videos via FFmpeg'.

This package implements the G-Formula method for causal inference with time-varying treatments and confounders using Bayesian multiple imputation methods, as described by Bartlett et al (2025) <doi:10.1177/09622802251316971>. It creates multiple synthetic imputed datasets under treatment regimes of interest using the mice package. These can then be analysed using rules developed for analysing multiple synthetic datasets.

This package provides a suite of tools for literature-based discovery in biomedical research. Provides functions for retrieving scientific articles from PubMed and other NCBI databases, extracting biomedical entities (diseases, drugs, genes, etc.), building co-occurrence networks, and applying various discovery models including ABC', AnC', LSI', and BITOLA'. The package also includes visualization tools for exploring discovered connections.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

Toolbox and shiny application to help researchers design movement ecology studies, focusing on two key objectives: estimating home range areas, and estimating fine-scale movement behavior, specifically speed and distance traveled. It provides interactive simulations and methodological guidance to support study planning and decision-making. The application is described in Silva et al. (2023) <doi:10.1111/2041-210X.14153>.

Comprehensively identifying states and state-like actors is difficult. This package provides data on states and state-like entities in the international system across time. The package combines and cross-references several existing datasets consistent with the aims and functions of the manydata package. It also includes functions for identifying state references in text, and for generating fictional state names.

Wraps the nametag library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.

This package provides a wrapper for optim for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1). Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006) <doi:10.1186/1471-2105-7-123>.

Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package HDoutliers', FastPCS() from the package FastPCS', mvBACON() from robustX', adjOutlyingness() from robustbase', DectectDeviatingCells() from cellWise', covMcd() from robustbase'.

R functions to access provenance information collected by rdt or rdtLite'. The information is stored inside a ProvInfo object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by rdt and rdtLite is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>.

This package provides a set of tools for determining the necessary sample size in order to identify the optimal dynamic treatment regime in a sequential, multiple assignment, randomized trial (SMART). Utilizes multiple comparisons with the best methodology to adjust for multiple comparisons. Designed for an arbitrary SMART design. Please see Artman (2018) <doi:10.1093/biostatistics/kxy064> for more details.

Supplies AnnotationHub with MassBank metabolite/compound annotations bundled in CompDb SQLite databases. CompDb SQLite databases contain general compound annotation as well as fragment spectra representing fragmentation patterns of compounds ions. MassBank data is retrieved from https://massbank.eu/MassBank and processed using helper functions from the CompoundDb Bioconductor package into redistributable SQLite databases.

The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine.

spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.

Psupertime is supervised pseudotime for single cell RNAseq data. It uses single cell RNAseq data, where the cells have a known ordering. This ordering helps to identify a small number of genes which place cells in that known order. It can be used for discovery of relevant genes, for identification of subpopulations, and characterization of further unknown or differently labelled data.

emacs-ranger is a minor mode that runs within dired, it emulates many of ranger's features. This minor mode shows a stack of parent directories, and updates the parent buffers, while you're navigating the file system. The preview window takes some of the ideas from Peep-Dired, to display previews for the selected files, in the primary dired buffer.

This package provides tools to measure connection and independence between variables without relying on linear models. Includes functions to compute Eta squared, Chi-squared, and Cramer V. The main advantage of this package is that it works without requiring parametric assumptions. The methods implemented are based on educational material and statistical decomposition techniques, not directly on previously published software or articles.

This package provides a simple approach to measure political sophistication based on open-ended survey responses. Discursive sophistication captures the complexity of individual attitude expression by quantifying its relative size, range, and constraint. For more information on the measurement approach see: Kraft, Patrick W. 2023. "Women Also Know Stuff: Challenging the Gender Gap in Political Sophistication." American Political Science Review (forthcoming).

It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.

Simplex optimization algorithms as firstly proposed by Spendley et al. (1962) <doi:10.1080/00401706.1962.10490033> and later modified by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> for laboratory and manufacturing processes. The package also provides tools for graphical representation of the simplexes and some example response surfaces that are useful in illustrating the optimization process.

This package provides a collection of methods for commonly undertaken analytical tasks, primarily developed for Public Health Scotland (PHS) analysts, but the package is also generally useful to others working in the healthcare space, particularly since it has functions for working with Community Health Index (CHI) numbers. The package can help to make data manipulation and analysis more efficient and reproducible.

Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the BigDataEurope project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.

Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are estimated using the Super Learner algorithm.

The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.