Reading and writing BibTeX files using data frames in R sessions.

Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional distances, trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships. Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.

Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>. It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.

Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <doi:10.48550/arXiv.1611.06649>.

Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at <https://genentech.github.io/BayesERbook/>.

Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) <doi:10.1023/A:1008824606259>.

Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.

This package provides a continuous date scale, omitting weekends and holidays.

Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed.

This package provides an interface to data provided by the Bank for International Settlements <https://www.bis.org>, allowing for programmatic retrieval of a large quantity of (central) banking data.

Generate urls and hyperlinks to commonly used biological databases and resources based on standard identifiers. This is primarily useful when writing dynamic reports that reference things like gene symbols in text or tables, allowing you to, for example, convert gene identifiers to hyperlinks pointing to their entry in the NCBI Gene database. Currently supports NCBI Gene, PubMed', Gene Ontology, KEGG', CRAN and Bioconductor.

Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2024) <doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al. (2024) <doi:10.48550/arXiv.2408.04327>.

This package provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.

Test the robustness of a user's Qualitative Comparative Analysis solutions to randomness, using the bootstrapped assessment: baQCA(). This package also includes a function that provides recommendations for improving solutions to reach typical significance levels: brQCA(). Data included come from McVeigh et al. (2014) <doi:10.1177/0003122414534065>.

This package implements the bolasso algorithm for consistent variable selection and estimation accuracy. Includes support for many parallel backends via the future package. For details see: Bach (2008), Bolasso: model consistent Lasso estimation through the bootstrap', <doi:10.48550/arXiv.0804.1302>.

Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies.

This package provides a client for retrieving data and metadata from major central bank APIs. It supports access to the Bundesbank SDMX Web Service API (<https://www.bundesbank.de/en/statistics/time-series-databases/help-for-sdmx-web-service/web-service-interface-data>), the Swiss National Bank Data Portal (<https://data.snb.ch/en>), the European Central Bank Data Portal API (<https://data.ecb.europa.eu/help/api/overview>), the Bank of England Interactive Statistical Database (<https://www.bankofengland.co.uk/boeapps/database>), the Banco de España API (<https://www.bde.es/webbe/en/estadisticas/recursos/api-estadisticas-bde.html>), the Bank for International Settlements SDMX Web Service (<https://stats.bis.org/api-doc/v1/>), the Banque de France Web Service (<https://webstat.banque-france.fr/en/pages/guide-migration-api/>), the Norges Bank SDMX Web Service (<https://www.norges-bank.no/en/topics/Statistics/open-data/>), the Oesterreichische Nationalbank Web Service (<https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>), and Bank of Canada Valet API (<https://www.bankofcanada.ca/valet/docs>).

The BioTIME database was first published in 2018 and inspired ideas, questions, project and research article. To make it even more accessible, an R package was created. The BioTIMEr package provides tools designed to interact with the BioTIME database. The functions provided include the BioTIME recommended methods for preparing (gridding and rarefaction) time series data, a selection of standard biodiversity metrics (including species richness, numerical abundance and exponential Shannon) alongside examples on how to display change over time. It also includes a sample subset of both the query and meta data, the full versions of which are freely available on the BioTIME website <https://biotime.st-andrews.ac.uk/home.php>.

Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) <doi:10.1177/00811750221112398>; Zang, Emma, et al., (2022) <doi:10.1093/geronb/gbab149>.

Fits boundary line models to datasets as proposed by Webb (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical inferences about their parameters. Provides additional tools for testing datasets for evidence of boundary presence and selecting initial starting values for model optimization prior to fitting the boundary line models. It also includes tools for conducting post-hoc analyses such as predicting boundary values and identifying the most limiting factor (Miti, Milne, Giller, Lark (2024) <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive analysis for datasets that exhibit upper boundary structures.

Fetches, harmonizes, and analyses data from the Spanish National Forest Inventory for reproducible, design-aware forest inventory workflows. Computes tree- and stand-level metrics, applies sampling-based expansion factors, estimates volume, and supports extensible processing for external inventory designs with custom sampling schemes and volume equations.

Provide a tool to easily build customized data flows to pre-process large volumes of information from different sources. To this end, bdpar allows to (i) easily use and create new functionalities and (ii) develop new data source extractors according to the user needs. Additionally, the package provides by default a predefined data flow to extract and pre-process the most relevant information (tokens, dates, ... ) from some textual sources (SMS, Email, YouTube comments).

Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.

Fetches monthly financial tables and banking sector data published on the official website of the Banking Regulation and Supervision Agency of Turkey and also enables you to save it as an Excel file. It is a R implementation of the Python package <https://pypi.org/project/bddkdata/>.