The sample size according to the Bethel's procedure.

Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).

Exploratory Bayesian factor analysis of continuous, mixed-type, and bounded continuous variables using the mode-jumping algorithm of Man and Culpepper (2020) <doi:10.1080/01621459.2020.1773833>.

Constructs treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any feasible linear model and the block design can be any feasible combination of crossed or nested block factors. The block design is a sum of one or more block factors and the block design is optimized sequentially with the levels of each successive block factor optimized conditional on all previously optimized block factors. D-optimality is used throughout except for square or rectangular lattice block designs which are constructed algebraically using mutually orthogonal Latin squares. Crossed block designs with interaction effects are optimized using a weighting scheme which allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500â 522 (2020) <doi:10.1007/s13253-020-00416-0>.

This package provides a Bayesian framework for estimating distributed lag linear and non-linear models. Model fitting is implemented using Integrated Nested Laplace Approximation (R package INLA'), together with prediction and visualization of exposure-lag-response associations. Additional functions allow estimation of optimal exposure values (e.g., minimum mortality temperature) and computation of attributable fractions and numbers. Models with crossbasis or onebasis terms are supported (R package dlnm').

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.

Arrays of structured data types can require large volumes of disk space to store. Blosc is a library that provides a fast and efficient way to compress such data. It is often applied in storage of n-dimensional arrays, such as in the case of the geo-spatial zarr file format. This package can be used to compress and decompress data using Blosc'.

US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex.

Business days calculations based on a list of holidays and nonworking weekdays. Quite useful for fixed income and derivatives pricing.

Extract data from Birdscan MR1 SQL vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the Birdscan MR1 databases are based on the dataset of Haest et al. (2021) <doi:10.5281/zenodo.5734960>). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the SQL databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) <doi:10.1111/ecog.04025>.

Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2025) <doi:10.1080/01621459.2025.2594191>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

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>.

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 JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees BEAST X software library of Baele et al (2025) <doi:10.1038/s41592-025-02751-x>. BEAST X supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the BEAST X JAR in this package, we offer an efficient distribution system for BEAST X use by other R packages using CRAN.

Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.

Compare dissolution profiles with confidence interval of similarity factor f2 using bootstrap methodology as described in the literature, such as Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997, ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. The package can also be used to simulate dissolution profiles based on mathematical modelling and multivariate normal distribution.

Objective Bayesian inference procedures for the parameters of the multivariate random effects model with application to multivariate meta-analysis. The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (<DOI:10.1214/20-BA1221>).

Bootstrap based goodness-of-fit tests. It allows to perform rigorous statistical tests to check if a chosen model family is correct based on the marked empirical process. The implemented algorithms are described in (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>) and can be applied to generalized linear models without any further implementation effort. As far as certain linearity conditions are fulfilled the resampling scheme are also applicable beyond generalized linear models. This is reflected in the software architecture which allows to reuse the resampling scheme by implementing only certain interfaces for models that are not supported natively by the package.

This package implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.

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>.

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.

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).

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) <doi:10.6339/23-JDS1092>.