Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.

Fits latent threshold model for simulated data and describes how to adjust model using real data. Implements algorithm proposed by Nakajima and West (2013) <doi:10.1080/07350015.2012.747847>. This package has a function to generate data, a function to configure priors and a function to fit the model. Examples may be checked inside the demonstration files.

Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.

An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. It controls false discovery rates through controlling the expected number of false discoveries.

This package provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)<doi:10.1111/arcm.12984>). It handles event typo-chronology based timespans defined by start/end date as well as more complex user-provided vector of probabilities.

Estimates Boltzmannâ Lotkaâ Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) <dx.doi.org/10.1098/rsif.2007.1288>.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). boinet is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.

An all-encompassing R toolkit designed to streamline the process of calling various bioinformatics software and then performing data analysis and visualization in R. With blit', users can easily integrate a wide array of bioinformatics command line tools into their workflows, leveraging the power of R for sophisticated data manipulation and graphical representation.

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>.

Visualizing the types and distribution of elements within bio-sequences. At the same time, We have developed a geom layer, geom_rrect(), that can generate rounded rectangles. No external references are used in the development of this package.

This package provides a set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method, including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with a corresponding set of S3 plot methods.

Carry out Bayesian estimation and forecasting for a variety of stochastic mortality models using vague prior distributions. Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265>, the Cairns-Blake-Dowd (2009) <doi:10.1080/10920277.2009.10597538>, the Li-Lee (2005) <doi:10.1353/dem.2005.0021>, and the Plat (2009) <doi:10.1016/j.insmatheco.2009.08.006> models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p à A à T (strata à age à year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported.

Bayesian estimation and variable selection for quantile regression models.

Statistical methods for analyzing binary replicates, which are noisy binary measurements of latent binary states. Provides scoring functions (average, median, likelihood-based, and Bayesian) to estimate the probability that an individual is in the positive state. Includes maximum a posteriori estimation via the EM algorithm and full Bayesian inference via Stan. Supports classification with inconclusive decisions and prevalence estimation.

Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. It should be emphasized that the package only extracts the data and facilitates its manipulation in R. Therefore, its sole purpose is to support empirical research. All data credits belong to SINESP, an integrated information platform developed and maintained by the National Secretariat of Public Security (SENASP) of the Ministry of Justice and Public Security. <https://www.gov.br/mj/pt-br/assuntos/sua-seguranca/seguranca-publica/sinesp-1>.

This package provides a framework for building interactive dashboards and document-based reports. Underlying data manipulation and visualization is possible using a web-based point and click user interface.

This package implements an S3 class based on data.table to store and process efficiently ethomics (high-throughput behavioural) data.

An interface to the Bayesian Weighted Sums model implemented in RStan'. It estimates the summed effect of multiple, often moderately to highly correlated, continuous predictors. Its applications can be found in analysis of exposure mixtures. The model was proposed by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) <doi:10.3390/ijerph18041373>. This implementation includes an extension to model binary outcome.

Fetches monthly financial tables and banking sector data published on the official website of the Banking Regulation and Supervision Agency of Turkey and also enables you to save it as an Excel file. It is a R implementation of the Python package <https://pypi.org/project/bddkdata/>.

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

This package provides functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.

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.

We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.