This package provides a framework and toolkit to guide R dashboard developers in implementing the Behavioral Insight Design (BID) framework. The package offers functions for documenting each of the five stages (Interpret, Notice, Anticipate, Structure, and Validate), along with a comprehensive concept dictionary. Works with both shiny applications and Quarto dashboards.

This package provides a recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.

Fit Bayesian time series models using Stan for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

This package provides a beginners toolbox to help those in ecology who want to deepen their understanding or utilize Bioacoustics in their work. The package has a number of utilizations from calculating frequency from waveform, performing operations in dB, and determining acoustic range of recorders. The majority of this package is based on key concepts learned from the K. Lisa Yang Center for Conservation Bioacoustics at Cornell University and their associated course: Introduction to Bioacoustics course. More information can be found within the walk through vignettes at <https://github.com/MattyD797/bioSNR/tree/main/vignettes>.

This package provides a client for retrieving data and metadata from central bank APIs including Banco de España (BdE), Banco de Portugal (BdP), Bank for International Settlements (BIS), Bank of Canada (BoC), Bank of England (BoE), Bank of Japan (BoJ), Banque de France (BdF), Czech National Bank (CNB), Deutsche Bundesbank (BBk), European Central Bank (ECB), National Bank of Poland (NBP), Norges Bank (NoB), Oesterreichische Nationalbank (OeNB), Sveriges Riksbank (SRb), and Swiss National Bank (SNB).

This package provides a set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.

Classical Boson Sampling using the algorithm of Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for generating random unitary matrices, evaluation of matrix permanents (both real and complex) and evaluation of complex permanent minors.

Client for the Binance <https://www.binance.com/> Spot, Futures, and Options REST APIs. Provides helper functions for signed and unsigned requests, market data retrieval, account access, and order management with data.table output by default.

Decomposition for differences-in-differences with variation in treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.

This package provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate Stan code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The bayesvl R package. Open Science Framework (May 18).

This package provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.

Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.

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

The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.

In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.

This package provides a collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines.

This package provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 <doi:10.1016/j.acha.2016.06.006>.

This package provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. An online version of the tool can be found at <http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>. Estimates of burden of disease aim to synthesize data about cause-specific morbidity and mortality through a systematic approach that enables evidence-based decisions and comparisons across settings. The focus of this tool is on four acute communicable diseases and syndromes, including Acute respiratory infections, Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.

Fit beta calibration models and obtain calibrated probabilities from them.

Implementation of Graph Signal Processing (GSP) methods including Spectral Graph Wavelet Transform (SGWT) for analyzing spatial patterns in biological data. Based on Hammond, Vandergheynst, and Gribonval (2011) <doi:10.1016/j.acha.2010.04.005>. Provides tools for multi-scale analysis of biology spatial signals, including forward and inverse transforms, energy analysis, and visualization functions tailored for biological applications. Biological application example is on Stephanie, Yao, Yuzhou (2024) <doi:10.1101/2024.12.20.629650>.

This package provides tools to estimate soil organic carbon stocks and sequestration rates in blue carbon ecosystems. BlueCarbon contains functions to estimate and correct for core compaction, estimate sample thickness, estimate organic carbon content from organic matter content, estimate organic carbon stocks and sequestration rates, and visualize the error of carbon stock extrapolation.

Determines effective sample size of a parametric prior distribution in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.

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>.

This package provides tools to deploy R web server applications that follow the _server.yml standard. This standard allows different R server frameworks ('plumber2', fiery', etc.) to be deployed using a common interface. The package supports deployment to DigitalOcean and includes validation tools to ensure _server.yml files are correctly formatted.