This package provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.

Implementation of BayesFlux.jl for R; It extends the famous Flux.jl machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks.

Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. References: Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013) <doi:10.1002/9781118818589>. Print ISBN: 9780470748466, Online ISBN: 9781118818589. © 2013 John Wiley & Sons Ltd.

Non-normality could greatly distort the meta-analytic results, according to the simulation in Sun and Cheung (2020) <doi: 10.3758/s13428-019-01334-x>. This package aims to detect non-normality for two independent groups with only limited descriptive statistics, including mean, standard deviation, minimum, and maximum.

This package provides a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

Several functions are provided to harmonize CN8 (Combined Nomenclature 8 digits) and PC8 (Production Communautaire 8 digits) product codes over time and the classification systems HS6 and BEC. Harmonization of CN8 codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 2021, respectively.

The conditional treatment effect for competing risks data in observational studies is estimated. While it is described as a constant difference between the hazard functions given the covariates, we do not assume specific functional forms for the covariates. Rava, D. and Xu, R. (2021) <arXiv:2112.09535>.

Plots path diagrams from models in lavaan using the plotting functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams via Graphviz', and these functions make it easy to generate these diagrams from a lavaan path model without having to write the DOT language graph specification.

This package provides a method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. See Li et al. (2023) "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". arXiv preprint <arXiv:2208.13074>.

Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.

Normalize a given Hadamard matrix. A Hadamard matrix is said to be normalized when its first row and first column entries are all 1, see Hedayat, A. and Wallis, W. D. (1978) "Hadamard matrices and their applications. The Annals of Statistics, 1184-1238." <doi:10.1214/aos/1176344370>.

This package provides a dataset of predefined color palettes based on the Star Trek science fiction series, associated color palette functions, and additional functions for generating customized palettes that are on theme. The package also offers functions for applying the palettes to plots made using the ggplot2 package.

This package provides a step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.

This package performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.

This package plots path diagrams from models in lavaan using the plotting functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams via Graphviz, and these functions make it easy to generate these diagrams from a lavaan path model without having to write the DOT language graph specification.

This package provides and extends the Fuzzy Coco algorithm by wrapping the FuzzyCoCo C++ Library, cf <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. Fuzzy Coco constructs systems that predict the outcome of a human decision-making process while providing an understandable explanation of a possible reasoning leading to it. The constructed fuzzy systems are composed of rules and linguistic variables. This package provides a S3 classic interface (fit_xy()/fit()/predict()/evaluate()) and a tidymodels'/'parsnip interface, a custom engine with custom iteration stop criterion and progress bar support as well as a systematic implementation that do not rely on genetic programming but rather explore all possible combinations.

This is an R package to build generic .loom files aligning with the default naming convention of the .loom format and to integrate other data types e.g.: regulons (SCENIC), clusters from Seurat, trajectory information... The package can also be used to extract data from .loom files.

This is a package to infer transmission trees from a dated phylogeny. It includes methods to simulate and analyze outbreaks. The methodology is described in Didelot et al. (2014) and Didelot et al. (2017).

This package provides the prediction() function, a type-safe alternative to predict() that always returns a data frame. The package currently supports common model types (e.g., "lm", "glm") from the stats package, as well as numerous other model classes from other add-on packages.

This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links.

This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects.

This package provides a developer-facing interface to the Arrow Database Connectivity ('ADBC') SQLite driver for the purposes of building high-level database interfaces for users. ADBC <https://arrow.apache.org/adbc/> is an API standard for database access libraries that uses Arrow for result sets and query parameters.

Implementation of Clarke's distribution-free test of non-nested models. Currently supported model functions are: lm(), glm() ('binomial', poisson', negative binomial links), polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet'). For more information on the test, see Clarke (2007) <doi:10.1093/pan/mpm004>.

Procedures for making continuous cartogram. Procedures available are: flow based cartogram (Gastner & Newman (2004) <doi:10.1073/pnas.0400280101>), fast flow based cartogram (Gastner, Seguy & More (2018) <doi:10.1073/pnas.1712674115>), rubber band based cartogram (Dougenik et al. (1985) <doi:10.1111/j.0033-0124.1985.00075.x>).