This package provides function shinyShortcut() that, when given the base directory of a shiny application, will produce an executable file that runs the shiny app directly in the user's default browser. Tested on both windows and unix machines. Inspired by and borrowing from <http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.

This package provides a tool to plot data with a large sample size using shiny and plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) <https://github.com/predict-idlab/plotly-resampler>.

This package provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010) <doi:10.1080/10705511003661553>.

Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The rjags package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within tidyverse or ggplot2 workflows. Low-level functions are also provided for creating new interfaces.

This package provides a comprehensive text annotation tool built with shiny'. Provides an interactive graphical user interface for coding text documents, managing code hierarchies, creating memos, and analyzing coding patterns. Features include code co-occurrence analysis, visualization of coding patterns, comparison of multiple coding sets, and export capabilities. Supports collaborative qualitative research through standardized annotation formats and analysis tools.

The package ABAEnrichment is designed to test for enrichment of user defined candidate genes in the set of expressed genes in different human brain regions. The core function aba_enrich integrates the expression of the candidate gene set (averaged across donors) and the structural information of the brain using an ontology, both provided by the Allen Brain Atlas project.

The Docker registry server enable you to host your own docker registry. With it, there is also two other utilities:

• The digest utility is a tool that generates checksums compatibles with various docker manifest files.

• The registry-api-descriptor-template is a tool for generating API specifications from the docs/spec/api.md.tmpl file.

Repeating of math operators at the broken line and the new line in inline equations is used in Cyrillic mathematical typography (Russian for example), but unfortunately LaTeX does not provide such an option. This package solves the problem by extending ideas described in M. I. Grinchuk TeX and Russian Traditions of Typesetting, and supports most of LaTeX mathematical packages.

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and DESeq2 (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

This package provides a simple interface to build designs using the package DeclareDesign'. In one line of code, users can specify the parameters of individual designs and diagnose their properties. The designers can also be used to compare performance of a given design across a range of combinations of parameters, such as effect size, sample size, and assignment probabilities.

An implementation for estimating Effective control to 50% of growth inhibition (EC50) for multi isolates and stratified datasets. It implements functions from the drc package in a way that is displayed a tidy data.frame as output. Info about the drc package is available in Ritz C, Baty F, Streibig JC, Gerhard D (2015) <doi:10.1371/journal.pone.0146021>.

Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>.

This package extends beachmat to support initialization of tatami matrices from HDF5-backed arrays. This allows C++ code in downstream packages to directly call the HDF5 C/C++ library to access array data, without the need for block processing via DelayedArray. Some utilities are also provided for direct creation of an in-memory tatami matrix from a HDF5 file.

The sparse nature of single cell epigenomics data can be overruled using probabilistic modelling methods such as Latent Dirichlet Allocation (LDA). This package allows the probabilistic modelling of cis-regulatory topics (cisTopics) from single cell epigenomics data, and includes functionalities to identify cell states based on the contribution of cisTopics and explore the nature and regulatory proteins driving them.

Retrying is a general-purpose retrying library to simplify the task of adding retry behavior to just about anything.

Features:

• Generic Decorator API.

• Specify stop condition (i.e. limit by number of attempts).

• Specify wait condition (i.e. exponential backoff sleeping between attempts).

• Customize retrying on Exceptions.

• Customize retrying on expected returned result.

Implementation of Bayesian models for spatial and spatio-temporal interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions. We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.

Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.

LecÈ iile prof/cls trebuie completate cu un câmp "ora", astfel ca oricare douÄ lecÈ ii prof/cls/ora sÄ nu se suprapunÄ Ã®ntr-o aceeaÈ i orÄ . The prof/cls lessons must be completed with a "hour" field ('ora), so that any two prof/cls/ora lessons do not overlap in the same hour. <https://vlad.bazon.net/>.

This package provides a finite-population significance test of the sharp causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population.

Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.

This package implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. The exact algorithm is implemented using pre-clustering with the k-means algorithm. Functions are also provided to search for all neighbors within a given distance. Parallelization is achieved for all methods using the BiocParallel framework.

Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.

This package provides a basic, clear implementation of tree-based gradient boosting designed to illustrate the core operation of boosting models. Tuning parameters (such as stochastic subsampling, modified learning rate, or regularization) are not implemented. The only adjustable parameter is the number of training rounds. If you are looking for a high performance boosting implementation with tuning parameters, consider the xgboost package.