Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003> and Combes et al. (2015) <doi:10.1016/j.quageo.2015.04.001>. This includes, amongst others, data import, export, application of age models and palaeodose model.

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:10.2307/2965583>). 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).

Extends blockr.core with interactive blocks for visual data wrangling using dplyr and tidyr operations. Users can build data transformation pipelines through a graphical interface without writing code directly. Includes blocks for filtering, selecting, mutating, summarizing, joining, and arranging data, with support for complex expressions, grouping operations, and real-time validation.

This package contains functions mainly focused to plotting bivariate maps.

This package implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) <doi:10.1007/b98835> and Bayesian principles (Gelman et al., 2013) <doi:10.1201/b16018>, the specific integration for economic disaggregation represents an original methodological contribution.

Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.

Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm the required action. It also provides functions that will install Github starred R packages whether available on CRAN or not.

The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.

Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.

Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed.

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 methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.

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.

This package contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Doses (BRAID) model of combined action, along with tools for implementing other combination analysis methods, including Bliss independence, combination index, and additional response surface methods.

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>.

This package implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) <doi:10.48550/arXiv.2408.10558>, and allows for the statistical modeling of multi-attribute pairwise comparison data.

Inflammation can affect many micronutrient biomarkers and can thus lead to incorrect diagnosis of individuals and to over- or under-estimate the prevalence of deficiency in a population. Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country partnership designed to improve the interpretation of nutrient biomarkers in settings of inflammation and to generate context-specific estimates of risk factors for anemia (Suchdev (2016) <doi:10.3945/an.115.010215>). In the past few years, BRINDA published a series of papers to provide guidance on how to adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum ferritin by Namaste (2020), soluble transferrin receptor (sTfR), serum zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by Namaste (2020) <doi:10.1093/ajcn/nqaa141>, Rohner (2017) <doi:10.3945/ajcn.116.142232>, McDonald (2020) <doi:10.1093/ajcn/nqz304>, and Young (2020) <doi:10.1093/ajcn/nqz303>. The BRINDA inflammation adjustment method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age Children (PSC); however, the general principle of the BRINDA method might apply to other population groups. The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method, as described above. The BRINDA R package will first carry out rigorous checks and provides users guidance to correct data or input errors (if they occur) prior to inflammation adjustments. After no errors are detected, the package implements the BRINDA inflammation adjustment for up to five micronutrient biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin, sTfR, and serum zinc (when appropriate), using inflammation indicators of AGP and/or CRP for various population groups. Of note, adjustment for serum and RBC folate and serum B-12 is not included in the R package, since evidence shows that no adjustment is needed for these micronutrient biomarkers in either WRA or PSC groups (Young (2020) <doi:10.1093/ajcn/nqz303>).

This package provides functions are pre-configured to utilize Bootstrap 5 classes and HTML structures to create Bootstrap-styled HTML quickly and easily. Includes functions for creating common Bootstrap elements such as containers, rows, cols, navbars, etc. Intended to be used with the html5 package. Learn more at <https://getbootstrap.com/>.

This package provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.

Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) <doi:10.1111/biom.13833>.

This package provides tools and code snippets for summarizing nested data, adverse events and REDCap study information.

Fast Bayesian inference of marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics, <doi:10.1111/biom.13064>.

Fits novel models for the conditional relative risk, risk difference and odds ratio <doi:10.1080/01621459.2016.1192546>.

This package provides functions to get and download city bike data from the website and API service of each city bike service in Norway. The package aims to reduce time spent on getting Norwegian city bike data, and lower barriers to start analyzing it. The data is retrieved from Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.