This package contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.

Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).

Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) <doi:10.1167/tvst.9.3.8>. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included shiny apps.

Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).

Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.

Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) <doi:10.1002/joc.3370060607>, Buishand, T. (1982) <doi:10.1016/0022-1694(82)90066-X>, Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) <doi:10.1016/j.atmosres.2020.104955>, Mann, H. B., Whitney, D. R. (1947) <doi:10.1214/aoms/1177730491>, Pettitt, A. N. (1979) <doi:10.2307/2346729>, Ruxton, G. D., jul (2006) <doi:10.1093/beheco/ark016>, Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) <doi:10.2151/jmsj1965.63.6_1157>.

Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication.

Tree- and rule-based models can be bagged (<doi:10.1007/BF00058655>) using this package and their predictions equations are stored in an efficient format to reduce the model objects size and speed.

Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the JAGS software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.

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>.

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>.

Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The bnviewer is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.

This package provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) <https://proceedings.mlr.press/v244/zambon24a.html>, analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) <doi: 10.48550/arXiv.2506.19554>.

This package implements bridge models for nowcasting and forecasting macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Simplifies forecasting and aggregating indicator variables to match the target frequency, enabling timely predictions ahead of official data releases. For more on bridge models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Burri (2023) <https://www5.unine.ch/RePEc/ftp/irn/pdfs/WP23-02.pdf> or Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>.

Resurrects the standard plot for shapes established by the base and graphics packages. This is suited to workflows that require plotting using the established and traditional idioms of plotting spatially coincident data where it belongs. This package depends on sf and only replaces the plot method.

This package provides a GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.

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

This package implements the Block-wise Rank in Similarity Graph Edge-count test (BRISE), a rank-based two-sample test designed for block-wise missing data. The method constructs (pattern) pair-wise similarity graphs and derives quadratic test statistics with asymptotic chi-square distribution or permutation-based p-values. It provides both vectorized and congregated versions for flexible inference. The methodology is described in Zhang, Liang, Maile, and Zhou (2025) <doi:10.48550/arXiv.2508.17411>.

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 (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

This package provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; a heatmap is produced to visually represent the similarity matrix. Optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters; the results of the clustering can be optionally used to sort the heatmap.

Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It may not function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems.

This package provides tools for bioinformatics modeling using recursive transformer-inspired architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. Includes utilities for cross-validation, calibration, benchmarking, and threshold optimization in predictive modeling workflows. The methodology builds on ensemble learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov 2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.