Bounded continuous data are encountered in many areas of test application. Examples include visual analogue scales used in the measurement of personality, mood, depression, and quality of life; item response times from tests with item deadlines; confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. The package draws on posterior sampling facilities provided by R-package rstan (Stan Development Team, 2025)<https://mc-stan.org/>. Available models include the Beta IRT model by Noel and Dauvier (2007)<doi:10.1177/0146621605287691>, the continuous response model by Samejima (1973)<doi:10.1007/BF03372160>, the unbounded normal model by Mellenbergh (1994)<doi:10.1207/s15327906mbr2903_2>, and the Simplex IRT model by Flores et al. (2020)<doi:10.1007/978-3-030-43469-4_8>. All models can be fitted with or without zero-one inflation (Molenaar et al., 2022)<doi:10.3102/10769986221108455>. Model fit comparisons can be conducted using the Watanabe-Akaike information criterion (WAIC), leave-one-out cross-validation information citerion (LOOIC) and the fully marginalized likelihood (i.e., Bayes factors).

This package implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>). The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.

This package provides tools to process the information obtained from experiments conducted in the Barnes Maze. These tools enable the detection of trajectories generated by subjects during trials, as well as the acquisition of precise coordinates and relevant statistical data regarding the results. Through this approach, it aims to facilitate the analysis and interpretation of observed behaviors, thereby contributing to a deeper understanding of learning and memory processes in such experiments.

Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.

Bayesian Latent Class Analysis using several different methods.

Perform seasonal adjustment and forecasting of weekly data. The package provides a user-friendly interface for computing seasonally adjusted estimates and forecasts of weekly time series and includes functions for the construction of country-specific prior adjustment variables, as well as diagnostic tools to assess the quality of the adjustments. The methodology is described in more detail in Ginker (2024) <doi:10.13140/RG.2.2.12221.44000>.

Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.

Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes to data, and functions to estimate the stochastic volatility process of a BSS process.

This package provides tools for working with BIDS (Brain Imaging Data Structure) formatted neuroimaging datasets. The package provides functionality for reading and querying BIDS'-compliant projects, creating mock BIDS datasets for testing, and extracting preprocessed data from fMRIPrep derivatives. It supports searching and filtering BIDS files by various entities such as subject, session, task, and run to streamline neuroimaging data workflows. See Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44> for the BIDS specification.

Generation of correlated artificial binary data.

Interface with the Brickset API <https://brickset.com/article/52664/api-version-3-documentation> for getting data about LEGO sets. Data sets that can be used for teaching and learning without the need of a Brickset account and API key are also included. Includes all LEGO since through the end of 2025.

BayesX performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the BayesX command line tool for easy installation. A convenient R interface is provided in package R2BayesX.

Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.

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.

This package provides a tuneable and interpretable method for relaxing the instrumental variables (IV) assumptions to infer treatment effects in the presence of unobserved confounding. For a treatment-associated covariate to be a valid IV, it must be (a) unconfounded with the outcome and (b) have a causal effect on the outcome that is exclusively mediated by the exposure. There is no general test of the validity of these IV assumptions for any particular pre-treatment covariate. However, if different pre-treatment covariates give differing causal effect estimates when treated as IVs, then we know at least some of the covariates violate these assumptions. budgetIVr exploits this fact by taking as input a minimum budget of pre-treatment covariates assumed to be valid IVs and idenfiying the set of causal effects that are consistent with the user's data and budget assumption. The following generalizations of this principle can be used in this package: (1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; (2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis; (3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost). The methods in this package require only summary statistics. Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature. For further methodological details, please refer to Penn et al. (2024) <doi:10.48550/arXiv.2411.06913>.

The Biodem package provides a number of functions for Biodemographic analysis.

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.

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

This package provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the adelie elastic-net solver with a glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. Under lasso penalization, lambda bounds the maximum covariate imbalance, so the regularization path traces a sequence of decreasing imbalance tolerances. For details, see Sverdrup & Hastie (2026) <doi:10.48550/arXiv.2602.18577>.

This package provides functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX.

This package provides a high-performance probabilistic programming library that aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU (Central Processing Unit), GPU (Graphics Processing Unit), or TPU (Tensor Processing Unit) using JAX (Just-In-Time compiled Accelerated linear algebra) as the computational backend: Sosa (2026) <doi:10.64898/2026.01.19.700318>.

Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to Moores, Pettitt & Mengersen (2020) <doi:10.1007/978-3-030-42553-1_6> for an overview and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details of specific algorithms.

Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176, <doi:10.1080/01621459.1997.10474074>). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data).

Fit semiparametric bivariate correlated frailty models.