This package provides tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Useful for logistic regression.

Extends blockr.core with interactive blocks for data visualization using ggplot2'. Users can build charts through a graphical interface without writing code directly. Includes common chart types (bar charts, line charts, pie charts, scatter plots) as well as statistical plots (boxplots, histograms, density plots, violin plots) with rich customization options and intuitive user interfaces.

Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to Moores, Pettitt & Mengersen (2020) <doi:10.1007/978-3-030-42553-1_6> for an overview and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details of specific algorithms.

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>).

It brings together several aspects of biodiversity data-cleaning in one place. bdc is organized in thematic modules related to different biodiversity dimensions, including 1) Merge datasets: standardization and integration of different datasets; 2) pre-filter: flagging and removal of invalid or non-interpretable information, followed by data amendments; 3) taxonomy: cleaning, parsing, and harmonization of scientific names from several taxonomic groups against taxonomic databases locally stored through the application of exact and partial matching algorithms; 4) space: flagging of erroneous, suspect, and low-precision geographic coordinates; and 5) time: flagging and, whenever possible, correction of inconsistent collection date. In addition, it contains features to visualize, document, and report data quality â which is essential for making data quality assessment transparent and reproducible. The reference for the methodology is Ribeiro and colleagues (2022) <doi:10.1111/2041-210X.13868>.

This package contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.

This package provides a lightweight package to access spatial basemaps from open sources such as OpenStreetMap', Carto', Mapbox and others in R.

Fit beta calibration models and obtain calibrated probabilities from them.

This package implements methods for difference-in-differences with bad controls, i.e., time-varying covariates that are affected by the treatment. Provides imputation, doubly robust, and machine learning estimators that are based on Caetano, Callaway, Payne, and Sant'Anna (2026) <doi:10.48550/arXiv.2608.03881>.

This package provides branding, theme application, and navigation utilities for applications built with bs4Dash and shiny'. Supports configurable sidebar brand display modes, hover-expand behavior, and theme customization using CSS variables. Includes complete navbar item helpers, navbar structure validation, reusable brand configuration, prototype top-navigation support, and helpers for common navigation bar and footer layouts.

The four functions svdcp() ('cp for column partitioned), svdbip() or svdbip2() ('bip for bipartitioned), and svdbips() ('s for a simultaneous optimization of a set of r solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) <https://eudml.org/doc/106424> and Hanafi M. & Lafosse, R. (2001) <https://eudml.org/doc/106494>.

When samples contain missing data, are small, or are suspected of bias, estimation of scale reliability may not be trustworthy. A recommended solution for this common problem has been Bayesian model estimation. Bayesian methods rely on user specified information from historical data or researcher intuition to more accurately estimate the parameters. This package provides a user friendly interface for estimating test reliability. Here, reliability is modeled as a beta distributed random variable with shape parameters alpha=true score variance and beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.

Trading of Butterfly Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (â The Bible of Options Strategies (2nd ed.)â , 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (â 151 Trading Strategiesâ , 2018, ISBN: 9783030027919). John C. Hull (â Options, Futures, and Other Derivatives (11th ed.)â , 2022, ISBN: 9780136939979).

As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in C++ and R.

To perform species identification using DNA barcodes.

Easily talk to Google's BigQuery Storage API from R (<https://docs.cloud.google.com/bigquery/docs/reference/storage/rpc>).

This package provides Brazilian municipality population estimates from official and research sources, with functions to aggregate the data by state, health region, sex, and age group.

Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.

Implementation of a statistical approach for estimating the joint health effects of multiple concurrent exposures, as described in Bobb et al (2015) <doi:10.1093/biostatistics/kxu058>.

This package provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

An R interface for the remote file hosting service Box (<https://www.box.com/>). In addition to uploading and downloading files, this package includes functions which mirror base R operations for local files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well as git style functions for entire directories (e.g. box_fetch(), box_push()).

This package provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.

This package provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

This package performs Bayesian unit root testing for time series models with maintained polynomial trend components as proposed by Chaturvedi and Kumar (2005) <doi:10.1016/j.spl.2005.04.044>. The package BayesURTrend computes posterior odds ratios, Bayes factors, and posterior probabilities for unit root hypotheses against stationary alternatives in autoregressive models augmented with polynomial trends. Methodological foundations for Bayesian unit root testing under structural breaks and maintained trends are drawn from Schotman and van Dijk (1991) <doi:10.1016/0304-4076(91)90038-F>, Phillips and Perron (1988) <doi:10.1093/biomet/75.2.335>, and Ouliaris et al. (1988) <doi:10.1007/978-94-009-2953-1_10>.