This is an implementation of design methods for binomial reliability demonstration tests (BRDTs) with failure count data. The acceptance decision uncertainty of BRDT has been quantified and the impacts of the uncertainty on related reliability assurance activities such as reliability growth (RG) and warranty services (WS) are evaluated. This package is associated with the work from the published paper "Optimal Binomial Reliability Demonstration Tests Design under Acceptance Decision Uncertainty" by Suiyao Chen et al. (2020) <doi:10.1080/08982112.2020.1757703>.

This package provides a collection of tools for regression analysis of non-negative data, including strictly positive and zero-inflated observations, based on the class of the Box-Cox symmetric (BCS) distributions and its zero-adjusted extension. The BCS distributions are a class of flexible probability models capable of describing different levels of skewness and tail-heaviness. The package offers a comprehensive regression modeling framework, including estimation and tools for evaluating goodness-of-fit.

Simulation of bivariate uniform data with a full range of correlations based on two beta densities and computation of the tetrachoric correlation (correlation of bivariate uniform data) from the phi coefficient (correlation of bivariate binary data) and vice versa.

Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed.

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).

This package provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.

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>.

Fits Cox model via stochastic gradient descent. This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using proximal stochastic gradient descent algorithm. For more details about the method, please see Aliasghar Tarkhan and Noah Simon (2020) <arXiv:2003.00116v2>.

Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials.

Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: rstan', INLA', spBayes', spTimer', spTDyn', CARBayes and CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

Providing equivalent functions for the dummy classifier and regressor used in Python scikit-learn library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the Python implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named dummy_regressor or dummy_classifier that can be used within the caret package pipeline.

Various tools dealing with batch effects, in particular enabling the removal of discrepancies between training and test sets in prediction scenarios. Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008) is implemented to enable integrating the quantile normalization step into prediction rules. The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide an additional function which enables a posteriori ('addon') batch effect removal in independent batches ('test data'). Here, the (already batch effect adjusted) training data is not altered. For evaluating the success of batch effect adjustment several metrics are provided. Moreover, the package implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba() and baaddon() which enable batch effect removal and addon batch effect removal, respectively, with one of the seven methods mentioned above. Another important function here is bametric() which is a wrapper function for all implemented methods for evaluating the success of batch effect removal. For (addon) quantile normalization and (addon) RMA normalization the functions qunormtrain(), qunormaddon(), rmatrain() and rmaaddon() can be used.

Can be used to read and write a fwf with an accompanying Blaise datamodel. Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a way to write and collect surveys and perform statistical analysis on the data. It stores its data in fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically interprets this metadata and reads the file into an R dataframe. When supplying a datamodel for writing, the dataframe will be automatically converted to that format and checked for compatibility. Supports dataframes, tibbles and LaF objects. For more information about Blaise', see <https://blaise.com/products/general-information>.

Estimation and interpretation of Bayesian distributed lag interaction models (BDLIMs). A BDLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a categorical variable under four specific patterns of modification. The main function is bdlim(). There are also summary and plotting files. Details on methodology are described in Wilson et al. (2017) <doi:10.1093/biostatistics/kxx002>.

This package provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) <doi:10.1111/rssb.12491>.

Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) <doi:10.1017/psrm.2025.10084> for examples and details of estimation.

Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential (Hursh & Silberberg, 2008, <doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data.

Implementation of No-Effect-Concentration estimation that uses brms (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter et al. (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating ECx values, but more particularly NEC (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>), NSEC (see Fisher and Fox (2023)<doi:10.1002/etc.5610>), and N(S)EC (see Fisher et al. 2023<doi:10.1002/ieam.4809>). A full description of this package can be found in Fisher et al. (2024)<doi:10.18637/jss.v110.i05>. This package expands and supersedes an original version implemented in R2jags (see Su and Yajima (2020)<https://CRAN.R-project.org/package=R2jags>; Fisher et al. (2020)<doi:10.5281/ZENODO.3966864>).

This package provides maximum likelihood estimates of the performance parameters that drive a binomial distribution of observed errors, and takes full advantage of zero error observations. High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility functions to work with the performance functions.

Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.

This package performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.

Defines the functions used to compute the bimodal index as defined by Wang et al. (2009) <https://pmc.ncbi.nlm.nih.gov/articles/PMC2730180/>, <doi:10.4137/CIN.S2846>.