This package implements the EM algorithm with one-step Gradient Descent method to estimate the parameters of the Block-Basu bivariate Pareto distribution with location and scale. We also found parametric bootstrap and asymptotic confidence intervals based on the observed Fisher information of scale and shape parameters, and exact confidence intervals for location parameters. Details are in Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199> "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale"; E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation"; Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> "An Introduction to the Bootstrap"; A P Dempster, N M Laird and D B Rubin (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete Data via the EM Algorithm".

This package provides a wrapper to allow users to download Bus Open Data Service BODS transport information from the API (<https://www.bus-data.dft.gov.uk/>). This includes timetable and fare metadata (including links for full datasets), timetable data at line level, and real-time location data.

Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).

This package provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) <doi:10.1111/rssb.12491>.

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

An implementation of methods for extracting a sparse unweighted network (i.e. a backbone) from an unweighted network (e.g., Hamann et al., 2016 <doi:10.1007/s13278-016-0332-2>), a weighted network (e.g., Serrano et al., 2009 <doi:10.1073/pnas.0808904106>), or a weighted projection (e.g., Neal et al., 2021 <doi:10.1038/s41598-021-03238-3>).

This package creates an area-proportional Venn diagram of 2 or 3 circles. BioVenn is the only R package that can automatically generate an accurate area-proportional Venn diagram by having only lists of (biological) identifiers as input. Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl IDs. In SVG mode, text and numbers can be dragged and dropped. Based on the BioVenn web interface available at <https://www.biovenn.nl>. Hulsen (2021) <doi:10.3233/DS-210032>.

Fit Bayesian multivariate GARCH models using Stan for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain rstan objects and can be examined with rstan functions.

This package provides a Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.

Building on the docking layout manager provided by blockr.dock', this provides an extension that allows for visualizing and manipulating a blockr board using a DAG-based user interface powered by the g6R graph visualisation HTML widget.

Generalization of the Bayesian classification and regression tree model that partitions subjects into terminal nodes and tailors predictive model to each terminal node.

Defines operating characteristics of Bayesian Adaptive Trials considering a generalised linear model response via Monte Carlo simulations of Bayesian GLM fitted via integrated Laplace approximations (INLA).

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.

Bindings to badgen <https://www.npmjs.com/package/badgen> to generate beautiful svg badges in R without internet access. Images can be converted to png using the rsvg package as shown in examples.

Detection of a statistically significant trend in the data provided by the user. This is based on the a signed test based on the binomial distribution. The package returns a trend test value, T, and also a p-value. A T value close to 1 indicates a rising trend, whereas a T value close to -1 indicates a decreasing trend. A T value close to 0 indicates no trend. There is also a command to visualize the trend. A test data set called gtsa_data is also available, which has global mean temperatures for January, April, July, and October for the years 1851 to 2022. Reference: Walpole, Myers, Myers, Ye. (2007, ISBN: 0-13-187711-9).

An interface for the Neo4j database providing mapping between different identifiers of biological entities. This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information. The method has been published by Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.

This package provides a system to build, visualise and evaluate Bayesian belief networks. The methods are described in Stafford et al. (2015) <doi:10.12688/f1000research.5981.1>.

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

This package provides a collection of S4 classes, methods and functions to create and visualize business plans. Different types of cash flows can be defined, which can then be used and tabulated to create profit and loss statements, cash flow plans, investment and depreciation schedules, loan amortization schedules, etc. The methods are designed to produce handsome tables in both PDF and HTML using RMarkdown or Shiny'.

Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> and Gemechu, Debusho and Haines (2025) <doi:10.5539/ijsp.v14n1p50> where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.

Quantile regression with cubic B-splines under monotonicity and convexity constraints using the Karlin-Studden SOCP formulation. The method is described in Abbes (2026) <doi:10.5281/zenodo.17427913>. This R implementation is intended for demonstration and prototyping; all B-spline and polynomial functions have been rewritten for consistency. A faster version written in Python is available at <https://github.com/alexandreabbes/Constrained-Quantile-Regression-with-cubic-splines>.

Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal <https://acikyesil.bursa.bel.tr/>. This will return all datasets stored in different formats.

This package provides comprehensive tools for Bayesian model diagnostics and comparison. Includes prior sensitivity analysis, posterior predictive checks (Gelman et al. (2013) <doi:10.1201/b16018>), advanced model comparison using Pareto-smoothed importance sampling leave-one-out cross-validation (Vehtari et al. (2017) <doi:10.1007/s11222-016-9696-4>), convergence diagnostics, and prior elicitation tools. Integrates with brms (Burkner (2017) <doi:10.18637/jss.v080.i01>), rstan', and rstanarm packages for comprehensive Bayesian workflow diagnostics.