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.

Biostatistical and clinical data analysis, including descriptive statistics, exploratory data analysis, sample size and power calculations, statistical inference, and data visualization. Normality tests are implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, omnibus test procedures are based on Blanca et al. (2017) <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), while sample size and power calculation methods follow Chow et al. (2017) <doi:10.1201/9781315183084>.

Data on the first 24 seasons of the UK TV show I'm a Celebrity, Get Me Out of Here', broadcast from 2002-2024. Taken from the Wikipedia pages for each season and the main page available at <https://en.wikipedia.org/wiki/I%27m_a_Celebrity...Get_Me_Out_of_Here!_(British_TV_series)>.

This package provides classes and functions to work with biological sequences (DNA, RNA and amino acid sequences). Implements S3 infrastructure to work with biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>. Provides a collection of functions to perform biological conversion among classes (transcription, translation) and basic operations on sequences (detection, selection and replacement based on positions or patterns). The package also provides functions to import and export sequences from and to other package formats.

Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.

Fits simultaneous regression models to compare two sources (reference and test) and estimates relative bioefficiency. Includes simultaneous exponential model with common asymptote (model = 1), slope-ratio model (model = 2), quadratic model (model = 3), linear-response plateau model (model = 4), and Michaelis-Menten model (model = 5). Output style follows the easyreg package. Methods are based on Finney (1978, ISBN:0-85264-252-0), Mercer et al. (1978) <doi:10.1093/jn/108.8.1244>, Robbins et al. (1979) <doi:10.1093/jn/109.10.1710>, Noll et al. (1984) <doi:10.3382/ps.0632458>, Gallant and Fuller (1973) <doi:10.1080/01621459.1973.10481356>, Littell et al. (1997) <doi:10.2527/1997.75102672x>, and Burnham and Anderson (2002, ISBN:978-0-387-95364-9).

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.

Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.

This package provides a set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) <doi:10.18637/jss.v100.i21>.

Tests for a linear relationship in the log ratio between an observed and simulated series and an independent variable. Typically this the error in modelled streamflow at an annual time scale, and a rainfall input. The approach allows for multiple sites as random factors and for multiple replicates of the simulated values. The approach is outlined in Gibbs et al. (2026) in review.

The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and brms is a powerful and versatile package for fitting Bayesian regression models. The brms.mmrm R package leverages brms to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.

Identifies cell-cell communication hotspots in spatial transcriptomics data using bivariate Local Moran's I statistics on hexagonally binned cells. Provides functions for spatial weighting, ligand-receptor pair filtering, hotspot detection, and visualisation of sender-receiver cell-type interactions.

Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.

Bayesian inference on the (discrete) power spectrum of time series.

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 provides functions to get and download city bike data from the website and API service of each city bike service in Norway. The package aims to reduce time spent on getting Norwegian city bike data, and lower barriers to start analyzing it. The data is retrieved from Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.

This package provides an R interface for the Bureau of Economic Analysis (BEA) API (see <http://www.bea.gov/API/bea_web_service_api_user_guide.htm> for more information) that serves two core purposes - 1. To Extract/Transform/Load data [beaGet()] from the BEA API as R-friendly formats in the user's work space [transformation done by default in beaGet() can be modified using optional parameters; see, too, bea2List(), bea2Tab()]. 2. To enable the search of descriptive meta data [beaSearch()]. Other features of the library exist mainly as intermediate methods or are in early stages of development. Important Note - You must have an API key to use this library. Register for a key at <http://www.bea.gov/API/signup/index.cfm> .

This package provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) <doi:10.1080/19466315.2024.2402275>, Schmitt and Baumann (2025) <doi:10.1080/19466315.2025.2486231>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>. For the latter two designs, the functions are mostly wrappers for functions provided by the package bhmbasket'.

Download, cache, and analyze annual microdata from the United States Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System (BRFSS) <https://www.cdc.gov/brfss/>. Each requested survey year is downloaded once as a compact file hosted on public releases, verified against a published checksum, and cached locally; queries then run through DuckDB (via the duckdb package), so column selection and repeat analyses never re-transfer data. Survey-design helpers construct srvyr design objects with year-appropriate weights, strata, and primary sampling units, including explicit handling of the 2011 weighting methodology change and of the codes CDC uses for missing-type answers.

Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case.

Simplify bivariate and regression analyses by automating result generation, including summary tables, statistical tests, and customizable graphs. It supports tests for continuous and dichotomous data, as well as stepwise regression for linear, logistic, and Firth penalized logistic models. While not a substitute for tailored analysis, BiVariAn accelerates workflows and is expanding features like multilingual interpretations of results.The methods for selecting significant statistical tests, as well as the predictor selection in prediction functions, can be referenced in the works of Marc Kery (2003) <doi:10.1890/0012-9623(2003)84[92:NORDIG]2.0.CO;2> and Rainer Puhr (2017) <doi:10.1002/sim.7273>.

This package provides functions to visualize combined action data in ggplot2'. Also provides functions for producing full BRAID analysis reports with custom layouts and aesthetics, using the BRAID method originally described in Twarog et al. (2016) <doi:10.1038/srep25523>.

This package provides an implementation of the product partition model described in Barry and Hartigan (2019) <doi:10.2307/2290726> for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) <doi:10.48550/arXiv.1509.00817>, allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented.

This package provides methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.