This package contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series.

Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.

Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.

Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables.

Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.

This package provides functions for modelling microbial inactivation under isothermal or dynamic conditions. The calculations are based on several mathematical models broadly used by the scientific community and industry. Functions enable to make predictions for cases where the kinetic parameters are known. It also implements functions for parameter estimation for isothermal and dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian approach to parameter estimation.

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>.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is a command-line tool. This package provides a way to call BEAST2 from an R function call.

This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

This package implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in <doi:10.1080/01621459.2015.1062383> (see also <doi:10.48550/arXiv.1405.0922>). For data matrices that are too large to operate on in memory, users can input objects with class ff (see the ff package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the parallel package can be used to parallelize the calculation of the bootstrap PCs.

Make some distributions from the C++ library Boost available in R'. In addition, the normal-inverse Gaussian distribution and the generalized inverse Gaussian distribution are provided. The distributions are represented by R6 classes. The method to sample from the generalized inverse Gaussian distribution is the one given in "Random variate generation for the generalized inverse Gaussian distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.

Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. <doi:10.1016/j.csda.2024.108053>) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.

Easily talk to Google's BigQuery Storage API from R (<https://cloud.google.com/bigquery/docs/reference/storage/rpc>).

This package performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in RcppArmadillo'. Parallel computation is available using OpenMP'.

An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, subsampling, and multimetric index calculation. Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral. As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) <https://archive.epa.gov/water/archive/web/html/index-14.html>.

The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).

The Biomarker Optimal Segmentation System R package, bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that bossR offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.

This package provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. An online version of the tool can be found at <http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>. Estimates of burden of disease aim to synthesize data about cause-specific morbidity and mortality through a systematic approach that enables evidence-based decisions and comparisons across settings. The focus of this tool is on four acute communicable diseases and syndromes, including Acute respiratory infections, Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.

Executes BASIC programs from the 1970s, for historical and educational purposes. This enables famous examples of early machine learning, artificial intelligence, natural language processing, cellular automata, and so on, to be run in their original form.

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

This package provides numerous utilities for acquiring and analyzing baseball data from online sources such as Baseball Reference <https://www.baseball-reference.com/>, FanGraphs <https://www.fangraphs.com/>, and the MLB Stats API <https://www.mlb.com/>.

Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>.

It contains some example datasets used in bibliometrix'. The data are bibliographic datasets exported from the SCOPUS (<https://scopus.com>) and Clarivate Analytics Web of Science (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package bibliometrix (<https://bibliometrix.org>).

This package provides methods for examining posterior MCMC samples from a single chain using trace plots and density plots, and from multiple chains by comparing posterior medians and credible intervals from each chain. These plotting functions have a variety of options, such as figure sizes, legends, parameters to plot, and saving plots to file. Functions interface with the NIMBLE software package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017) <doi:10.1080/10618600.2016.1172487>.