This package provides functions to create side-by-side boxplots for a continuous variable grouped by a two-level categorical variable, check normality assumptions using the Shapiro-Wilk test (Shapiro and Wilk (1965) <doi:10.2307/2333709>), and perform appropriate statistical tests such as the independent two-sample t-test (Student (1908) <doi:10.1093/biomet/6.1.1>) or the Mannâ Whitney U test ( Mannâ Whitney (1947) <doi:10.1214/aoms/1177730491>). Returns a publication-ready plot and test statistics including test statistic, degrees of freedom, and p-value.

Shows statistics about bytes contained in a file as a circle graph of deviations from mean in sigma increments. The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc).

Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.

The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.

This package provides arithmetic functions for R matrix and big.matrix objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects.

This package provides a platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.

Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return. CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577). Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132). Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143). Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).

This package provides functions for downloading data from the Bank for International Settlements (BIS; <https://www.bis.org/>) in Basel. Supported are only full datasets in (typically) CSV format. The package is lightweight and without dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.

An interface for the Neo4j database providing mapping between different identifiers of biological entities. This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information. The method has been published by Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.

This package creates bubbles within shiny and rmarkdown backgrounds using the bubbly-bg JavaScript library.

Estimates survival and mortality with covariates from census or capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots, as described in Colchero, Jones, and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and Colchero et al. (2021) <doi:10.1038/s41467-021-23894-3>.

Simulate multivariate data with arbitrary marginal distributions. bigsimr is a package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. It utilizes the Julia package Bigsimr.jl for its core routines.

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a modelâ s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely BART Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, dbarts (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and bartMachine (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

This package contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) <doi:10/ghtfz9>), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) <doi:10/jzkt>), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) <doi:10/ghpnx8>). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers.

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 package provides a suite of functions for finance, including the estimation of variance matrices via a statistical factor model or Ledoit-Wolf shrinkage.

The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. A development version is on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful for any univariate, irregularly sampled Gaussian time-series, but animal movement analysts are encouraged to apply correlated velocity change point analysis as implemented in the smoove package, as of this writing on GitHub at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis is provided in the UnivariateBCPA vignette.

It provides access to and information about the most important Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>, the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g. generating dynamic reports with a complete analysis of a series) and exporting these time series.

This package provides an accessible and robust implementation of core BME methodologies for spatial prediction. It enables the systematic integration of heterogeneous data sources including both hard data (precise measurements) and soft interval data (bounded or uncertain observations) while incorporating prior knowledge and supporting variogram-based spatial modeling. The BME methodology is described in Christakos (1990) <doi:10.1007/BF00890661> and Serre and Christakos (1999) <doi:10.1007/s004770050029>.

An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including JAGS', BUGS', MCMCpack', and Stan'.

Waffle plots are rectangular pie charts that represent a quantity or abundances using colored squares or other symbol. This makes them better at transmitting information as the discrete number of squares is easier to read than the circular area of pie charts. While the original waffle charts were rectangular with 10 rows and columns, with a single square representing 1%, they are nowadays popular in various infographics to visualize any proportional ratios.

This package performs change point detection on univariate and multivariate time series (Martà nez & Mena, 2014, <doi:10.1214/14-BA878> ; Corradin, Danese & Ongaro, 2022, <doi:10.1016/j.ijar.2021.12.019>) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, <doi:10.1007/s11222-025-10756-x>).

This package implements efficient NumPy'-like broadcasted operations for atomic and recursive arrays. In the context of operations involving 2 (or more) arrays, â broadcastingâ refers to efficiently recycling array dimensions, without making copies. Besides linking to Rcpp', broadcast does not use any external libraries in any way; broadcast was essentially made from scratch and can be installed out-of-the-box. The implementations available in broadcast include, but are not limited to, the following. 1) Broadcasted element-wise operations on any 2 arrays; they support a large set of relational, arithmetic, Boolean, string, and bit-wise operations. 2) A faster, more memory efficient, and broadcasted abind-like function, for binding arrays along an arbitrary dimension. 3) Broadcasted ifelse-like and apply-like functions. 4) Casting functions, that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa. 5) A few linear algebra functions for statistics. The functions in the broadcast package strive to minimize computation time and memory usage (which is not just better for efficient computing, but also for the environment).