This package provides R bindings to the dockview JavaScript library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or Quarto or in shiny apps <https://shiny.posit.co/>. In shiny mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.

This package provides a parallel backend for the %dopar% function using the parallel package.

Estimates dose-response relations from summarized dose-response data and to combines them according to principles of (multivariate) random-effects models.

This package provides a toolbox to create and manage metadata files and configuration profiles: files used to configure the parameters and initial settings for some computer programs.

Modifies dot plots to have different sizes of dots mimicking violin plots and identifies modes or peaks for them based on frequency and kernel density estimates (Rosenblatt, 1956) <doi:10.1214/aoms/1177728190> (Parzen, 1962) <doi:10.1214/aoms/1177704472>.

This package provides a wrapper for the download.file function, making it possible to download files over HTTPS across platforms. The RCurl package provides this functionality (and much more) but has external dependencies. This package has is implemented purely in R.

This package performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.

It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.

Microsoft Word docx files provide an XML structure that is fairly straightforward to navigate, especially when it applies to Word tables and comments. Tools are provided to determine table count/structure, comment count and also to extract/clean tables and comments from Microsoft Word docx documents. There is also nascent support for .doc and .pptx files.

Create quick and easy dot-and-whisker plots of regression results. It takes as input either (1) a coefficient table in standard form or (2) one (or a list of) fitted model objects (of any type that has methods implemented in the parameters package). It returns ggplot objects that can be further customized using tools from the ggplot2 package. The package also includes helper functions for tasks such as rescaling coefficients or relabeling predictor variables. See more methodological discussion of the visualization and data management methods used in this package in Kastellec and Leoni (2007) <doi:10.1017/S1537592707072209> and Gelman (2008) <doi:10.1002/sim.3107>.

Access diverse ggplot2'-compatible color palettes for simplified data visualization.

Build a Dockerfile straight from your R session. dockerfiler allows you to create step by step a Dockerfile, and provide convenient tools to wrap R code inside this Dockerfile.

This package provides the user with an interactive application which can be used to facilitate the planning of dose finding studies by applying the theory of optimal experimental design.

Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package rsm', Latin hypercube samples from packages lhs and DiceDesign', and D-optimal designs from package AlgDesign have been implemented.

The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset).

This package creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation.

The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).

This package provides a wrapper on top of the Domino Data Python SDK library. It lets you query and access Domino Data Sources directly from your R environment. Under the hood, Domino Data R SDK leverages the API provided by the Domino Data Python SDK', which must be installed as a prerequisite. Domino is a platform that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows. See <https://docs.dominodatalab.com/en/latest/api_guide/140b48/domino-data-api> for more information.

Implement download buttons in HTML output from rmarkdown without the need for runtime:shiny'.

Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.

The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions.

An experiment data package associated with the publication Dona et al. (2013). Package contains runnable vignettes showing an example image segmentation for one posterior lateral line primordium, and also the data table and code used to analyze tissue-scale lifetime-ratio statistics.

Constructs dynamic optimal shrinkage estimators for the weights of the global minimum variance portfolio which are reconstructed at given reallocation points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>). Two dynamic shrinkage estimators are available in this package. One using overlapping samples while the other use nonoverlapping samples.

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.