This package provides a collection of functions that perform operations on time-series accelerometer data, such as identify the non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented.

Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. interacCircos are inspired by circosJS', BioCircos.js and NG-Circos and we integrate the modules of circosJS', BioCircos.js and NG-Circos into this R package, based on htmlwidgets framework.

Add-on to the airGR package which provides the tools to assimilate observed discharges in daily GR hydrological models. The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) <doi:10.1029/2020WR028390>.

If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.

Separates diffusive and ebullitive (bubble) fluxes from continuous concentration measurements using a running variance approach. Ebullitive events are identified when the running variance exceeds a user-set threshold. Diffusive fluxes are calculated via linear regression on the non-ebullitive portion of the data. See Sø et al. (2024) <doi:10.1029/2024JG008035> for details.

This package provides functions for SNP-phased allele-specific methylation (ASM) analysis across the 41 canonical human imprinted differentially methylated regions (DMRs). Reads are assigned to REF or ALT alleles based on bisulfite-aware SNP detection, enabling diagnosis of imprinting disorders from whole-genome bisulfite sequencing data. <https://github.com/19-saha/ImprintCapASM>.

The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].

This package provides a collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of LeSage and Pace (2009, ISBN: 1420064258) were ported and adjusted for R, as described in Wilhelm and de Matos (2013) <doi:10.32614/RJ-2013-013>.

Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) <doi:10.1007/s42081-023-00221-2> and Taketomi et al.(2024-)(under review).

a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name.

Extends blockr.core with interactive blocks for data visualization using ggplot2'. Users can build charts through a graphical interface without writing code directly. Includes common chart types (bar charts, line charts, pie charts, scatter plots) as well as statistical plots (boxplots, histograms, density plots, violin plots) with rich customization options and intuitive user interfaces.

This package provides functions to randomly select, return, and print quotes or entire scenes from the American version of the show the Office. Receive laughs from one of of the greatest sitcoms of all time on demand. Add these functions to your .Rprofile to get a good laugh everytime you start a new R session.

This package creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.

This package provides functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.

Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029).

This package provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectationâ maximization (EM) algorithms. The package supports Wiener and Ornsteinâ Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM.

New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.

This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis.

This package provides a fast, scalable, and versatile framework for simulating large systems with Gillespie's Stochastic Simulation Algorithm (SSA). This package is the spiritual successor to the GillespieSSA package. Benefits of this package include major speed improvements (>100x), easier to understand documentation, and many unit tests that try to ensure the package works as intended.

This package provides a comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, criminology, social and economic studies related to criminal behavior. Datasets span global and local contexts, with a mix of tabular and spatial data.

It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) <doi:10.1002/sim.8092> and Gómez & Lagakos (2013) <doi:10.1002/sim.5547>.

This package provides tools to simulate genetic distance matrices, align and compare them via multidimensional scaling (MDS) and Procrustes, and evaluate imputation with the Bootstrapping Evaluation for Structural Missingness Imputation (BESMI) framework. Methods align with Zhu et al. (2025) <doi:10.3389/fpls.2025.1543956> and the associated software resource Zhu (2025) <doi:10.26188/28602953>.

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.

Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.