Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.

Allows to connect selectizeInputs widgets as filters to a reactable table. As known from spreadsheet applications, column filters are interdependent, so each filter only shows the values that are really available at the moment based on the current selection in other filters. Filter values currently not available (and also those being available) can be shown via popovers or tooltips.

Making specification curve analysis easy, fast, and pretty. It improves upon existing offerings with additional features and tidyverse integration. Users can easily visualize and evaluate how their models behave under different specifications with a high degree of customization. For a description and applications of specification curve analysis see Simonsohn, Simmons, and Nelson (2020) <doi:10.1038/s41562-020-0912-z>.

This package performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079).

When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.

AgiMicroRna provides useful functionality for the processing, quality assessment and differential expression analysis of Agilent microRNA array data. The package uses a limma-like structure to generate the processed data in order to make statistical inferences about differential expression using the linear model features implemented in limma. Standard Bioconductor objects are used so that other packages could be used as well.

Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This package provides tools to calculate these limits on the basis of the mathematical properties of the distribution of the analyzed items.

This package provides an htmlwidgets interface to billboard.js, a re-usable easy interface JavaScript chart library, based on D3 v4+. Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts. All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in shiny apps.

NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

This package contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025a) <doi:10.1080/09332480.2025.2473291>. Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs.

Given a patient-sharing network, calculate either the classic care density as proposed by Pollack et al. (2013) <doi:10.1007/s11606-012-2104-7> or the fragmented care density as proposed by Engels et al. (2024) <doi:10.1186/s12874-023-02106-0>. By utilizing the igraph and data.table packages, the provided functions scale well for very large graphs.

Duplicated data can exist in different rows and columns and user may need to treat observations (rows) connected by duplicated data as one observation, e.g. companies can belong to one family (and thus: be one company) by sharing some telephone numbers. This package allows to find connected rows based on data on chosen columns and collapse it into one row.

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>).

Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.

Computes the most important properties of four Bayesian early gating designs (two single arm and two randomized controlled designs), such as minimum required number of successes in the experimental group to make a GO decision, operating characteristics and average operating characteristics with respect to the sample size. These might aid in deciding what design to use for the early phase trial.

Converts R data frames and sf spatial objects into JSON and GeoJSON strings. The core encoders are implemented in Rust using the extendr framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized JSON text, allowing applications such as shiny or web APIs to transfer data to client-side JavaScript libraries without additional encoding overhead.

Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R.

This package provides tools for model specification in the latent variable framework (add-on to the lava package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.

This package provides tools of Bayesian analysis framework using the method suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.

Overcomes one of the major challenges in mobile (passive) sensing, namely being able to pre-process the raw data that comes from a mobile sensing app, specifically m-Path Sense <https://m-path.io>. The main task of mpathsenser is therefore to read m-Path Sense JSON files into a database and provide several convenience functions to aid in data processing.

Agricultural data for 1888-2021 from the Morrow Plots at the University of Illinois. The world's second oldest ongoing agricultural experiment, the Morrow Plots measure the impact of crop rotation and fertility treatments on corn yields. The data includes planting information and annual yield measures for corn grown continuously and in rotation with other crops, in treated and untreated soil.

Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).

Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.