This package provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.

This package implements the distance measure for mixed-scale variables proposed by Buttler and Fickel (1995), based on normalized mean pairwise distances (Gini mean difference), and an R2 statistic to assess clustering quality.

R bindings for the various functions and statistical distributions provided by the Boost Math library <https://www.boost.org/doc/libs/latest/libs/math/doc/html/index.html>.

This package provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from JAGS and Stan models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating JAGS and bridgesampling syntax to basic functions such as rng, quantile, and distribution functions.

Barnard's unconditional test for 2x2 contingency tables.

Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.

This package provides a suite of tools to mitigate sampling bias in species occurrence records by thinning data in the environmental space (E-space). This process can improve the accuracy and precision of species distribution models (SDM, also known as ecological niche models, ENM). The package offers a data-driven protocol to determine thinning parameters using kernel-density bandwidth selection. Two thinning methods are provided (stochastic and deterministic) to reduce over-sampled environmental conditions and down-weight outlier observations. The name bean reflects the core principle of the method: each pod (a grid cell in E-space) is allowed to contain only a limited number of beans (occurrence points). See Silverman (1986, ISBN:978-0-412-24620-3) and Rousseeuw and Leroy (2003, ISBN:978-0-471-48855-2) for the underlying statistical methods.

Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential (Hursh & Silberberg, 2008, <doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data.

This package provides functions for calculating biochemical methane potential (BMP) from laboratory measurements and other types of data processing and prediction useful for biogas research. Raw laboratory measurements for diverse methods (volumetric, manometric, gravimetric, gas density) can be processed to calculate BMP. Theoretical maximum BMP or methane or biogas yield can be predicted from various measures of substrate composition. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to standard (or user-defined) temperature and pressure. Gas quantity can be converted between volume, mass, and moles. A function for planning BMP experiments can consider multiple constraints in suggesting substrate or inoculum quantities, and check for problems. Inoculum and substrate mass can be determined for planning BMP experiments. Finally, a set of first-order models can be fit to measured methane production rate or cumulative yield in order to extract estimates of ultimate yield and kinetic constants. See Hafner et al. (2018) <doi:10.1016/j.softx.2018.06.005> for details. OBA is a web application that provides access to some of the package functionality: <https://biotransformers.shinyapps.io/oba1/>. The Standard BMP Methods website documents the calculations in detail: <https://www.dbfz.de/en/BMP>.

Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, <https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players percentiles, plots of players monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls, offensive rebounds and different types of shooting charts. Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.

This package provides a convenience package for use while drafting code. It facilitates making stand-out comment lines decorated with bands of characters. The input text strings are converted into R comment lines, suitably formatted. These are then displayed in a console window and, if possible, automatically transferred to a clipboard ready for pasting into an R script. Designed to save time when drafting R scripts that will need to be navigated and maintained by other programmers.

This package implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).

This package provides a comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.

This is an implementation of BART:Bayesian Additive Regression Trees, by Chipman, George, McCulloch (2010).

Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.

This package provides a highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>, Solymos et al. (2013) <doi:10.1111/2041-210X.12106>, and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>, and sound attenuation experiments by Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.

Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in <doi:10.48550/arXiv.2404.05118>. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.

Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. bibliometrix provides various routines for importing bibliographic data from SCOPUS', Clarivate Analytics Web of Science (<https://www.webofknowledge.com/>), Digital Science Dimensions (<https://www.dimensions.ai/>), OpenAlex (<https://openalex.org/>), Cochrane Library (<https://www.cochranelibrary.com/>), Lens (<https://lens.org>), and PubMed (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

This package implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package metafor to obtain parameter estimates and likelihoods, so installation of R package metafor is required. This approach refers to the studies of Anscombe (1956) <doi:10.2307/2332926>, Haldane (1940) <doi:10.2307/2332614>, Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).

The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.

This package provides methods for estimation and hypothesis testing of proportions in group testing designs: methods for estimating a proportion in a single population (assuming sensitivity and specificity equal to 1 in designs with equal group sizes), as well as hypothesis tests and functions for experimental design for this situation. For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations.

Set of functions to perform various bootstrap unit root tests for both individual time series (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>, Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>, Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.

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.