Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.

Calculates B-value and empirical equivalence bound. B-value is defined as the maximum magnitude of a confidence interval; and the empirical equivalence bound is the minimum B-value at a certain level. A new two-stage procedure for hypothesis testing is proposed, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using the introduced empirical equivalence bound. See Zhao et al. (2019) "B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing" <arXiv:1912.13084> for details.

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.

Read and process brand.yml YAML files. brand.yml is a simple, portable YAML file that codifies your company's brand guidelines into a format that can be used by Quarto', Shiny and R tooling to create branded outputs. Maintain unified, branded theming for web applications to printed reports to dashboards and presentations with a consistent look and feel.

This package provides a Metropolis-coupled Markov chain Monte Carlo sampler, post-processing and parameter estimation functions, and plotting utilities for the generalized graded unfolding model of Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>.

This package provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.

This package provides functions to prepare tidy objects from estimated models via BVAR (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and visualisation thereof. Bridges the gap between estimating models with BVAR and plotting the results in a more sophisticated way with ggplot2 as well as passing them on in a tidy format.

This package implements the EM algorithm with one-step Gradient Descent method to estimate the parameters of the Block-Basu bivariate Pareto distribution with location and scale. We also found parametric bootstrap and asymptotic confidence intervals based on the observed Fisher information of scale and shape parameters, and exact confidence intervals for location parameters. Details are in Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199> "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale"; E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation"; Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> "An Introduction to the Bootstrap"; A P Dempster, N M Laird and D B Rubin (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete Data via the EM Algorithm".

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Simulating synthetic clumped isotope dataset, fitting linear regression models under Bayesian and non-Bayesian frameworks, and generating temperature reconstructions for the same two approaches. Please note that models implemented in this package are described in Roman-Palacios et al. (2021) <doi:10.1002/essoar.10507995.1>.

Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, <https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players percentiles, plots of players monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls, offensive rebounds and different types of shooting charts. Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.

This package provides methods for detecting and visualizing cladogenic shifts in multivariate trait data on phylogenies. Implements penalized-likelihood multivariate generalized least squares models, enabling analyses of high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Includes a greedy step-wise shift-search algorithm following approaches developed in Smith et al. (2023) <doi:10.1111/nph.19099> and Berv et al. (2024) <doi:10.1126/sciadv.adp0114>. Methods build on multivariate GLS approaches described in Clavel et al. (2019) <doi:10.1093/sysbio/syy045> and implemented in the mvgls() function from the mvMORPH package. Documentation and vignettes are available at <https://jakeberv.com/bifrost/>, including the introductory vignette at <https://jakeberv.com/bifrost/articles/jaw-shape-vignette.html>.

Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.

It computes betas-select, coefficients after standardization in structural equation models and regression models, standardizing only selected variables. Supports models with moderation, with product terms formed after standardization. It also offers confidence intervals that account for standardization, including bootstrap confidence intervals as proposed by Cheung et al. (2022) <doi:10.1037/hea0001188>.

This package provides a system of functions and datasets to carry out quantitative analyses in the biological sciences. The package facilitates data management, exploratory analyses, and model assessment. Although it currently focuses on forest ecology, silviculture and decision-making, most of the package functions are applicable across several disciplines, including economics, environmental science, and healthcare.

This package creates an interactive graphics interface to visualize backtest results of different financial instruments, such as equities, futures, and credit default swaps. The package does not run backtests on the given data set but displays a graphical explanation of the backtest results. Users can look at backtest graphics for different instruments, investment strategies, and portfolios. Summary statistics of different portfolio holdings are shown in the left panel, and interactive plots of profit and loss (P&L), net market value (NMV) and gross market value (GMV) are displayed in the right panel.

Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.

This is a port of the WTC MATLAB package written by Aslak Grinsted and the wavelet program written by Christopher Torrence and Gibert P. Compo. This package can be used to perform univariate and bivariate (cross-wavelet, wavelet coherence, wavelet clustering) analyses.

Barnard's unconditional test for 2x2 contingency tables.

Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, JAGS software and rjags package are required, and users need to pre-install them.

Calculates several entropy metrics for spatial data inspired by Boltzmann's entropy formula. It includes metrics introduced by Cushman for landscape mosaics (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), and landscape gradients and point patterns (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>); and by Gao et al. for landscape gradients (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, BMRB offers an R package RBMRB to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, BMRBr File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the BMRB have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual BMRB data file from the BMRB database <http://www.bmrb.wisc.edu>. The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402â 8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13â 22. <doi:10.1007/s10858-005-1717-0>.

Search and access more than ten thousand datasets included in BCRPDATA (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

This package provides a GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.