Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.

This package performs exploratory data analysis and variable screening for binary classification models using weight-of-evidence (WOE) and information value (IV). In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV).

The data analysis module for the Iterative Optimization Heuristics Profiler ('IOHprofiler'). This module provides statistical analysis methods for the benchmark data generated by optimization heuristics, which can be visualized through a web-based interface. The benchmark data is usually generated by the experimentation module, called IOHexperimenter'. IOHanalyzer also supports the widely used COCO (Comparing Continuous Optimisers) data format for benchmarking.

This package provides functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depend on the suggested ctmcd package.

It includes functions to download and process the Planet NICFI (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. GDAL (library for raster and vector geospatial data formats) and aria2c (paralleled download utility) must be installed and configured in the user's Operating System.

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.

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>.

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.

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).

The clusterCrit package provides an implementation of the following indices: Czekanowski-Dice, Folkes-Mallows, Hubert Γ, Jaccard, McNemar, Kulczynski, Phi, Rand, Rogers-Tanimoto, Russel-Rao or Sokal-Sneath. ClusterCrit defines several functions which compute internal quality indices or external comparison indices. The partitions are specified as an integer vector giving the index of the cluster each observation belongs to.

This package contains a number of comparative "phylogenetic" methods, mostly focusing on analysing diversification and character evolution. Contains implementations of "BiSSE" (Binary State Speciation and Extinction) and its unresolved tree extensions, "MuSSE" (Multiple State Speciation and Extinction), "QuaSSE", "GeoSSE", and "BiSSE-ness" Other included methods include Markov models of discrete and continuous trait evolution and constant rate speciation and extinction.

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>).

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.

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.

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.

Allows for painless use of the Metopio health atlas APIs <https://metopio.com/how-it-works/atlas/> to explore and import data. Metopio health atlases store open public health data. See what topics (or indicators) are available among specific populations, periods, and geographic layers. Download relevant data along with geographic boundaries or point datasets. Spatial datasets are returned as sf objects.

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.

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.

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.

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.

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>.