Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our JSS paper (2016, <doi:10.18637/jss.v071.i02>. A sufficiently recent version of Protocol Buffers library is required; currently version 3.3.0 from 2017 is the tested minimum.

This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses.

This package offers a flexible statistical simulator for scRNA-seq data. It can generate data that captures gene correlation. Additionally, it allows for varying the number of cells and sequencing depth.

This package provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e., static .html files). It currently supports linked brushing and filtering.

This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network.

Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts.

Automates regression testing of package allelematch'. Over 2500 tests cover all functions in allelematch', reproduce the examples from the documentation, and include negative tests. The implementation is based on testthat'.

This package provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division <https://datasf.org/opendata/>.

Gives convenient access to publicly available police-recorded open crime data from large cities in the United States that are included in the Crime Open Database <https://osf.io/zyaqn/>.

This package provides a Shiny application and supporting functions for the design and analysis of consistency tests based on Kappa statistic with categorical responses. Wraps irr and kappaSize packages.

Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) <doi:10.1378/chest.13-1328>.

Expectile and quantile regression of models with nonlinear effects e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes as well as boosting; also supplies expectiles for common distributions.

This package provides functions to implement the formula instrument method in Borusyak and Hull (2023) <doi:10.3982/ECTA19367> and examine its sensitivity to the assumed distributional of counterfactual shocks.

Automated feature engineering functions tailored for credit scoring. It includes utilities for extracting structured features from timestamps, IP addresses, and email addresses, enabling enhanced predictive modeling for financial risk assessment.

Analytics to read in and segment raw GENEActiv accelerometer data into epochs and events. For more details on the GENEActiv device, see <https://activinsights.com/resources/geneactiv-support-1-2/>.

This package contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.

Computation of generalized hypergeometric function with tunable high precision in a vectorized manner, with the floating-point datatypes from mpfr or gmp library. The computation is limited to real numbers.

This package implements partial transfer learning (PTL) for causal effect estimation using source and target data, with bootstrap-based source detection. Provides data generating processes and nuisance functions for simulation.

Draw 2 dimensional and three dimensional plot for multiple regression models using package ggplot2 and rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).

Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.

Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).

This package creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. treeheatr utilizes the customizable ggparty package for drawing decision trees.

Download and compile any version of the IANA Time Zone Database (also known as Olson database) and make it current in your R session. Beware: on Windows Cygwin is required!

Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.