This package implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) <doi:10.1109/TVCG.2016.2598839> "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with sf for spatial data and ggplot2 for visualization. Supports temporal/streaming data analysis.

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

This package provides a molecular genetics tool that processes binary data from fragment analysis. It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: <doi:10.1016/j.biocontrol.2020.104426>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) <doi:10.1111/1365-2745.12869>, and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) <doi:10.1016/j.aquabot.2021.103372>. The authors of these publications are thanked for allowing the use of their data.

Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition).

Bayesian purity model to estimate tumor purity using methylation array data (DNA methylation Infinium 450K array data) without reference samples.

This package provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) <doi:10.1016/j.csda.2021.107345>.

This package provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.

Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for fitting and diagnosing bent-cable regressions for autoregressive time-series data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time series or otherwise - Chiu, Lockhart and Routledge 2006, <doi:10.1198/016214505000001177>). Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub.

This package provides tools to create, validate, and export BioCompute Objects described in King et al. (2019) <doi:10.17605/osf.io/h59uh>. Users can encode information in data frames, and compose BioCompute Objects from the domains defined by the standard. A checksum validator and a JSON schema validator are provided. This package also supports exporting BioCompute Objects as JSON, PDF, HTML, or Word documents, and exporting to cloud-based platforms.

Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the brms package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).

Fit Bayesian time series models using Stan for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.

This package provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.

This package contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) <doi:10/ghtfz9>), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) <doi:10/jzkt>), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) <doi:10/ghpnx8>). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers.

This package provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) <doi:10.1198/016214502388618906> and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>. Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques.

Based on â Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysisâ (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit.

Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349â 361, <doi:10.5194/hess-27-349-2023>, 2023.

The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission.

Useful libraries for building a Java based GUI under R are provided.

This package creates compact letter displays (CLDs) for pairwise comparisons from statistical post-hoc tests. Groups sharing the same letter are not significantly different from each other. Supports multiple input formats including results from stats pairwise tests, DescTools', PMCMRplus', rstatix', symmetric matrices of p-values, and data frames. Provides a consistent interface for visualizing statistical groupings across different testing frameworks.

Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) <doi:10.1111/2041-210X.13152>.

Estimates tree crown scorch from terrestrial lidar scans collected with a RIEGL vz400i. The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, <doi:10.1186/s42408-025-00420-0>).

Generates the scripts required to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database and associated documentation for supported database platforms. Leverages the SqlRender package to convert the Data Definition Language (DDL) script written in parameterized Structured Query Language (SQL) to the other supported dialects.

This package provides a comprehensive reproducibility framework designed for R and bioinformatics workflows. Automatically captures the entire analysis environment including R session info, package versions, external tool versions ('Samtools', STAR', BWA', etc.), conda environments, reference genomes, data provenance with smart checksumming for large files, parameter choices, random seeds, and hardware specifications. Generates executable scripts with Docker', Singularity', and renv configurations. Integrates with workflow managers ('Nextflow', Snakemake', WDL', CWL') to ensure complete reproducibility of computational research workflows.