Building on the docking layout manager provided by dockViewR', this provides a flexible front-end to blockr.core'. It provides an extension mechanism which allows for providing means to manipulate a board object via panel-based user interface components.

This package provides a method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

This package provides a collection of models for bivariate alternating recurrent event data analysis. Includes non-parametric and semi-parametric methods.

Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.

Computes uniform bounds on the distance between the cumulative distribution function of a standardized sum of random variables and its first-order Edgeworth expansion, following the article Derumigny, Girard, Guyonvarch (2023) <doi:10.1007/s13171-023-00320-y>.

Simulation of bivariate uniform data with a full range of correlations based on two beta densities and computation of the tetrachoric correlation (correlation of bivariate uniform data) from the phi coefficient (correlation of bivariate binary data) and vice versa.

Search and access more than ten thousand datasets included in BCRPDATA (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources.

Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.

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 contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).

This package provides functions and data sets reproducing some examples in Box, Hunter and Hunter II. Useful for statistical design of experiments, especially factorial experiments.

The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.

This package performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) <doi:10.1080/10618600.2018.1458625>) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), <doi:10.1080/10618600.2022.2129662>). Incorporates OpenMP and SNOW parallelization and utilizes C'/'C++ under the hood.

This package provides a set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method, including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with a corresponding set of S3 plot methods.

Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>.

This package provides a developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.

Description: Provides streamlined tools for retrieving sequences from NCBI, performing sequence alignments (pairwise and multiple), and building phylogenetic trees. Implements the Needleman-Wunsch algorithm for global alignment (Needleman & Wunsch (1970) <doi:10.1016/0022-2836(70)90057-4>), Smith-Waterman for local alignment (Smith & Waterman (1981) <doi:10.1016/0022-2836(81)90087-5>), and Neighbor-Joining for tree construction (Saitou & Nei (1987) <doi:10.1093/oxfordjournals.molbev.a040454>).

This package provides a tool to perform all different statistical tests and calculations needed by Biological dosimetry Laboratories. Detailed documentation is available in <https://biodosetools-team.github.io/documentation/>.

Creating, rendering and writing BPMN diagrams <https://www.bpmn.org/>. Functionalities can be used to visualize and export BPMN diagrams created using the pm4py and bupaRminer packages. Part of the bupaR ecosystem.

This package provides an interface to Bank of Japan <https://www.boj.or.jp> statistics.

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the broom package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

From a given data frame, this package learns its Bayesian network structure based on a selected score.

Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.