This package implements the Beta Kernel Process (BKP) for nonparametric modeling of spatially varying binomial probabilities, together with its extension, the Dirichlet Kernel Process (DKP), for categorical or multinomial data. The package provides functions for model fitting, predictive inference with uncertainty quantification, posterior simulation, and visualization in one-and two-dimensional input spaces. Multiple kernel functions (Gaussian, Matern 5/2, and Matern 3/2) are supported, with hyperparameters optimized through multi-start gradient-based search. For more details, see Zhao, Qing, and Xu (2025) <doi:10.48550/arXiv.2508.10447>.

Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through OpenMP <https://gcc.gnu.org/wiki/openmp>.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.

This package provides tools to process the information obtained from experiments conducted in the Barnes Maze. These tools enable the detection of trajectories generated by subjects during trials, as well as the acquisition of precise coordinates and relevant statistical data regarding the results. Through this approach, it aims to facilitate the analysis and interpretation of observed behaviors, thereby contributing to a deeper understanding of learning and memory processes in such experiments.

This package provides a Bayesian model for examining the association between environmental mixtures and all Taxa measured in a hierarchical microbiome dataset in a single integrated analysis. Compared with analyzing the associations of environmental mixtures with each Taxa individually, BaHZING controls Type 1 error rates and provides more stable effect estimates when dealing with small sample sizes.

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.

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).

Identifies genome-related molecular traits with significant evidence of genetic regulation and performs a bootstrap procedure to correct estimated effect sizes for over-estimation present in cis-QTL mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 2018 <doi: 10.1093/nar/gky780>.

It offers simplified access to Brazilian macroeconomic and financial indicators selected from official sources, such as the IBGE (Brazilian Institute of Geography and Statistics) via the SIDRA API and the Central Bank of Brazil via the SGS API. It allows users to quickly retrieve and visualize data series such as the unemployment rate and the Selic interest rate. This package was developed for data access and visualization purposes, without generating forecasts or statistical results. For more information, see the official APIs: <https://sidra.ibge.gov.br/> and <https://dadosabertos.bcb.gov.br/dataset/>.

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.

Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisherâ s Exact Test on significant parameters.

Perform record linkage on streaming files using recursive Bayesian updating.

Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations (Carragher et al. (2020) <doi:10.1002/sim.8563>). This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (<https://gtr.ukri.org/>). Principal Investigator: Raymond Carragher.

This package provides functions to compute pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.

Noise filter based on determining the proportion of neighboring points. A false point will be rejected if it has only few neighbors, but accepted if the proportion of neighbors in a rectangular frame is high. The size of the rectangular frame as well as the cut-off value, i.e. of a minimum proportion of neighbor-points, may be supplied or can be calculated automatically. Originally designed for the cleaning of heart rates, but suitable for filtering any slowly-changing physiological variable.For more information see Signer (2010)<doi:10.1111/j.2041-210X.2009.00010.x>.

Adjust the Gamma regression models from a Bayesian perspective described by Cepeda and Urdinola (2012) <doi:10.1080/03610918.2011.600500>, modeling the parameters of mean and shape and using different link functions for the parameter associated to the mean. And calculates different adjustment statistics such as the Akaike information criterion and Bayesian information criterion.

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, BMRB offers an R package RBMRB to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, BMRBr File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the BMRB have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual BMRB data file from the BMRB database <http://www.bmrb.wisc.edu>. The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402â 8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13â 22. <doi:10.1007/s10858-005-1717-0>.

To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.

An interactive document on the topic of binary logistic regression analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.

Estimates VAR and VARX models with Structured Penalties.

This package provides a framework and toolkit to guide R dashboard developers in implementing the Behavioral Insight Design (BID) framework. The package offers functions for documenting each of the five stages (Interpret, Notice, Anticipate, Structure, and Validate), along with a comprehensive concept dictionary. Works with both shiny applications and Quarto dashboards.

This package provides a lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding <https://www.rfc-editor.org/rfc/rfc7932>. Brotli is similar in speed to deflate (gzip) but offers more dense compression.

An umbrella package providing a phenotype/genotype data structure and scalable and efficient computational methods for large genomic datasets in combination with several other packages: BEDMatrix', LinkedMatrix', and symDMatrix'.

This package provides a computational tool to describe patterns in black and white images from natural structures. bwimage implemented functions for exceptionally broad subject. For instance, bwimage provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.