This package contains data sets regarding songs on the Billboard Hot 100 list from 1960 to 2016. The data sets include the ranks for the given year, musical features of a lot of the songs and lyrics for several of the songs as well.

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the broom package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

This package provides functions to combine data on voting blocs size, turnout, and vote choice to estimate each bloc's vote contributions to the Democratic and Republican parties. The package also includes functions for uncertainty estimation and plotting. Users may define voting blocs along a discrete or continuous variable. The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.

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.

Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.

Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) <doi:10.1023/A:1008824606259>.

Flags and checks occurrence data that are in Darwin Core format. The package includes generic functions and data as well as some that are specific to bees. This package is meant to build upon and be complimentary to other excellent occurrence cleaning packages, including bdc and CoordinateCleaner'. This package uses datasets from several sources and particularly from the Discover Life Website, created by Ascher and Pickering (2020). For further information, please see the original publication and package website. Publication - Dorey et al. (2023) <doi:10.1101/2023.06.30.547152> and package website - Dorey et al. (2023) <https://github.com/jbdorey/BeeBDC>.

Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.

Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate and life expectancy (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).

Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.

This package implements the Bayesian Clustering Factor Models (BCFM) for simultaneous clustering and latent factor analysis of multivariate longitudinal data. The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via Rcpp'. Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) <doi:10.1002/sim.70350>.

This package provides a simple tool to quantify the amount of transmission of an infectious disease of interest occurring within and between population groups. bumblebee uses counts of observed directed transmission pairs, identified phylogenetically from deep-sequence data or from epidemiological contacts, to quantify transmission flows within and between population groups accounting for sampling heterogeneity. Population groups might include: geographical areas (e.g. communities, regions), demographic groups (e.g. age, gender) or arms of a randomized clinical trial. See the bumblebee website for statistical theory, documentation and examples <https://magosil86.github.io/bumblebee/>.

Selecting linear and generalized linear models for large data sets using modified stepwise procedure and modern selection criteria (like modifications of Bayesian Information Criterion). Selection can be performed on data which exceed RAM capacity. Bogdan et al., (2004) <doi:10.1534/genetics.103.021683>.

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.

This project aims to enable the method of Path Analysis to infer causalities from data. For this we propose a hybrid approach, which uses Bayesian network structure learning algorithms from data to create the input file for creation of a PA model. The process is performed in a semi-automatic way by our intermediate algorithm, allowing novice researchers to create and evaluate their own PA models from a data set. The references used for this project are: Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>. Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>. Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>. Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.

This package implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables.

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

This package creates an interactive graphics interface to visualize backtest results of different financial instruments, such as equities, futures, and credit default swaps. The package does not run backtests on the given data set but displays a graphical explanation of the backtest results. Users can look at backtest graphics for different instruments, investment strategies, and portfolios. Summary statistics of different portfolio holdings are shown in the left panel, and interactive plots of profit and loss (P&L), net market value (NMV) and gross market value (GMV) are displayed in the right panel.

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.

This package provides a cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.

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

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. breakaway is the premier package for statistical analysis of microbial diversity. breakaway implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.