This package provides comprehensive tools for blinded sample size re-estimation (BSSR) in two-arm clinical trials with binary endpoints. Unlike traditional fixed-sample designs, BSSR allows adaptive sample size adjustments during trials while maintaining statistical integrity and study blinding. Implements five exact statistical tests: Pearson chi-squared, Fisher exact, Fisher mid-p, Z-pooled exact unconditional, and Boschloo exact unconditional tests. Supports restricted, unrestricted, and weighted BSSR approaches with exact Type I error control. Statistical methods based on Mehrotra et al. (2003) <doi:10.1111/1541-0420.00051> and Kieser (2020) <doi:10.1007/978-3-030-49528-2_21>.

Data on the first 24 seasons of the UK TV show I'm a Celebrity, Get Me Out of Here', broadcast from 2002-2024. Taken from the Wikipedia pages for each season and the main page available at <https://en.wikipedia.org/wiki/I%27m_a_Celebrity...Get_Me_Out_of_Here!_(British_TV_series)>.

The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) <arXiv:1009.3977>.

This package provides a collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.

Reads several formats of 13C data (IRIS/Wagner, BreathID) and CSV. Creates artificial sample data for testing. Fits Maes/Ghoos, Bluck-Coward self-correcting formula using nls', nlme'. Methods to fit breath test curves with Bayesian Stan methods are refactored to package breathteststan'. For a Shiny GUI, see package dmenne/breathtestshiny on github.

The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions. The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters. The BN-PSD model can be used to yield complex population structures. This simulation approach is now extended to subpopulations related by a tree. Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.

Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>.

Reproducible and automated analysis of multiplex bead assays such as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi Biotec 2014; Application note: Data acquisition and analysis without the MACSQuant analyzer; <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The package provides functions for streamlined reading of fcs files, and identification of bead clusters and analyte expression. The package eases the calculation of standard curves and the subsequent calculation of the analyte concentration.

Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. Covariate adjustment and Bayesian model averaging is supported. Functions are provided to easily obtain inference on the dose-response relationship and plot the dose-response curve.

This package provides consistent batch means estimation of Monte Carlo standard errors.

An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed. d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships between all pairs of variables of a certain(s) type(s) in a multivariate data set.

This package contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) <doi:10/ghtfz9>), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) <doi:10/jzkt>), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) <doi:10/ghpnx8>). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers.

This package provides squared semi partial correlations, tolerance, Mahalanobis, Likelihood Ratio Chi Square, and Pseudo R Square. Aberson, C. L. (2022) <doi:10.31234/osf.io/s2yqn>.

This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).

Bayesian Additive Regression Kernels (BARK) provides an implementation for non-parametric function estimation using Levy Random Field priors for functions that may be represented as a sum of additive multivariate kernels. Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 <doi:10.1214/11-AOS889>.

This package provides functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2', Tracer and DensiTree'. babette provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from R'.

Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).

Bayesian Latent Class Analysis using several different methods.

Analyse single case analyses against a control group. Its purpose is to provide a flexible, with good power and low first type error approach that can manage at the same time controls and patient's data. The use of Bayesian statistics allows to test both the alternative and null hypothesis. Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq> Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.

Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.

Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) <doi:10.1177/00811750221112398>; Zang, Emma, et al., (2022) <doi:10.1093/geronb/gbab149>.

This package provides a lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding <https://www.rfc-editor.org/rfc/rfc7932>. Brotli is similar in speed to deflate (gzip) but offers more dense compression.