The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.

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>.

The binomialRF is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, binomialRF then tests whether a feature is selected more often than by random chance.

This package provides a client for cryptocurrency exchange BitMEX <https://www.bitmex.com/> including the ability to obtain historic trade data and place, edit and cancel orders. BitMEX's Testnet and live API are both supported.

This package implements a wide variety of one- and two-parameter Bayesian CRM designs. The program can run interactively, allowing the user to enter outcomes after each cohort has been recruited, or via simulation to assess operating characteristics. See Sweeting et al. (2013): <doi:10.18637/jss.v054.i13>.

Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian aggregator that is regularized by analyzing the forecasters disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters probability predictions (p) of a future binary event and b) the forecasters common prior (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example.

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/>.

Working with reproducible reports or any other similar projects often require to run the script that builds the output file in a specified way. buildr can help you organize, modify and comfortably run those scripts. The package provides a set of functions that interactively guides you through the process and that are available as RStudio Addin, meaning you can set up the keyboard shortcuts, enabling you to choose and run the desired build script with one keystroke anywhere anytime.

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

An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.

Bayesian methods for predicting the calendar time at which a target number of events is reached in clinical trials. The methodology applies to both blinded and unblinded settings and jointly models enrollment, event-time, and censoring processes. The package provides tools for trial data simulation, model fitting using Stan via the rstan interface, and event time prediction under a wide range of trial designs, including varying sample sizes, enrollment patterns, treatment effects, and event or censoring time distributions. The package is intended to support interim monitoring, operational planning, and decision-making in clinical trial development. Methods are described in Fu et al. (2025) <doi:10.1002/sim.70310>.

Stan-based curve-fitting function for use with package breathtestcore by the same author. Stan functions are refactored here for easier testing.

Frequentist inference on adaptively generated data. The methods implemented are based on Zhan et al. (2021) <doi:10.48550/arXiv.2106.02029> and Hadad et al. (2021) <doi:10.48550/arXiv.1911.02768>. For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied.

This package provides functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(...) for the main example of how to use this package.

This package provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.

Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.

This package implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles (Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for economic disaggregation represents an original methodological contribution.

This package provides tools to create, validate, and export BioCompute Objects described in King et al. (2019) <doi:10.17605/osf.io/h59uh>. Users can encode information in data frames, and compose BioCompute Objects from the domains defined by the standard. A checksum validator and a JSON schema validator are provided. This package also supports exporting BioCompute Objects as JSON, PDF, HTML, or Word documents, and exporting to cloud-based platforms.

This package implements the efficient estimator of bid-ask spreads from open, high, low, and close prices described in Ardia, Guidotti, & Kroencke (JFE, 2024) <doi:10.1016/j.jfineco.2024.103916>. It also provides an implementation of the estimators described in Roll (JF, 1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>, Corwin & Schultz (JF, 2012) <doi:10.1111/j.1540-6261.2012.01729.x>, and Abdi & Ranaldo (RFS, 2017) <doi:10.1093/rfs/hhx084>.

Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2019) <doi:10.1080/03610926.2018.1425450> for details.

For studying recurrent disease and death with competing risks, comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events. Alternatively, comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. This package implements a nonparametric estimator for the conditional cumulative incidence function and a nonparametric conditional bivariate cumulative incidence function for the bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.

This package performs brace expansions on strings. Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string. The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.

Bell regression models for count data with overdispersion. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) <doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020) <doi:10.1080/02664763.2019.1636940>.

This package implements three test procedures using bootstrap resampling techniques for assessing treatment effects in one-way ANOVA models with unequal variances (heteroscedasticity). It includes a parametric bootstrap likelihood ratio test (PB_LRT()), a pairwise parametric bootstrap mean test (PPBMT()), and a Rademacher wild pairwise non-parametric bootstrap test (RWPNPBT()). These methods provide robust alternatives to classical ANOVA and standard pairwise comparisons when the assumption of homogeneity of variances is violated.