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>.

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

This package provides topic modeling and visualization by interfacing with the BERTopic library for Python via reticulate'. See Grootendorst (2022) <doi:10.48550/arXiv.2203.05794>.

Plotting package based on the grid system, combining elements of a bubble plot and heatmap to conveniently display two numerical variables, (represented by color and size) grouped by categorical variables on the x and y axes. This is a useful alternative to a forest plot when the data can be grouped in two dimensions, such as predictors x outcomes. It has particular advantages for visualising the metabolic measures produced by the Nightingale Health metabolomics platform, and templates are included for automatically generating figures from these datasets.

This package provides a developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.

Create a blended curve from two survival curves, which is particularly useful for survival extrapolation in health technology assessment. The main idea is to mix a flexible model that fits the observed data well with a parametric model that encodes assumptions about long-term survival. The two curves are blended into a single survival curve that is identical to the first model over the range of observed times and gradually approaches the parametric model over the extrapolation period based on a given weight function. This approach allows for the inclusion of external information, such as data from registries or expert opinion, to guide long-term extrapolations, especially when dealing with immature trial data. See Che et al. (2022) <doi:10.1177/0272989X221134545>.

Create a hierarchical acoustic event species classifier out of multiple call type detectors as described in Rankin et al (2017) <doi:10.1111/mms.12381>.

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".

Interactive box plot using plotly for clinical trial analysis.

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subjectâ s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.

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.

Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in <doi:10.1016/j.annepidem.2006.04.001> and <doi:10.1177/0272989X09353452>.

This package provides Bayesian methods for comparing groups on multiple binary outcomes. Includes basic tests using multivariate Bernoulli distributions, subgroup analysis via generalized linear models, and multilevel models for clustered data. For statistical underpinnings, see Kavelaars, Mulder, and Kaptein (2020) <doi:10.1177/0962280220922256>, Kavelaars, Mulder, and Kaptein (2024) <doi:10.1080/00273171.2024.2337340>, and Kavelaars, Mulder, and Kaptein (2023) <doi:10.1186/s12874-023-02034-z>. An interactive shiny app to perform sample size computations is available.

BabyTime is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time.

This package provides a method to filter correlation and covariance matrices by averaging bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet, Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning (2020) <arXiv:2005.08703>.

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 worked examples for the jaw-shape dataset.

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

This package implements the four-parameter Beta-Danish distribution and its three-parameter Exponentiated Danish submodel for survival, reliability and lifetime data analysis, following Ahmad and Danish (2025) <doi:10.2478/jamsi-2025-0010>. Density, distribution, quantile, survival, hazard and random generation functions are evaluated so as to retain accuracy in the heavy upper tail, where the survival function is regularly varying. Estimation covers maximum likelihood for complete and right-censored samples, ridge-penalized fitting for weakly identified regimes, a grouped likelihood for times recorded on a coarse grid, and Bayesian sampling. Inference provides log-scale Wald and profile likelihood intervals, together with a reparameterization in terms of the identified composite of the two shape parameters. Structural properties include raw, incomplete and conditional moments with their existence conditions, Shannon, Renyi and Tsallis entropies, mean residual life, mean deviations, Lorenz and Bonferroni curves, probability weighted moments, order statistics, stress-strength reliability, hazard shape classification and the tail index. Regression modules cover accelerated failure time models, mixture and promotion-time cure models, and competing risks with Aalen-Johansen comparison and Gray's test. Analyses can be run directly from a delimited text file or spreadsheet.

This package implements Bayesian response-adaptive randomization methods based on Bayesian hypothesis testing for multi-arm settings (Pawel and Held, 2025, <doi:10.48550/arXiv.2510.01734>).

This package provides a blind spike program provides samples to a laboratory in order to perform quality control (QC) checks. The samples provided are of a known quantity to the tester. The laboratory is typically uninformed of that the sample provided is a QC sample.

Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function.

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/>.

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys) is implemented. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.