Add functionality to create drag and drop div elements in shiny.

The steepness package computes steepness as a property of dominance hierarchies. Steepness is defined as the absolute slope of the straight line fitted to the normalized David's scores. The normalized David's scores can be obtained on the basis of dyadic dominance indices corrected for chance or by means of proportions of wins. Given an observed sociomatrix, it computes hierarchy's steepness and estimates statistical significance by means of a randomization test.

Simple Component Analysis (SCA) often provides much more interpretable components than Principal Components (PCA) while still representing much of the variability in the data.

This package provides a collection of functions that creates graphs with error bars for data collected from one-way or higher factorial designs.

Offers markdown output formats designed with various styles, allowing users to generate HTML reports tailored for scientific or machine learning showcase. The output has a contemporary appearance with vibrant visuals, providing numerous styles for effective highlighting. Created using the tufte <https://rstudio.github.io/tufte/> package code as a starting point.

The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.

Includes built-in methods for generating long SQL CASE statements, and other SQL statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to SQL'-like databases, such as when using the RODBC package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements.

Message translation is often managed with po files and the gettext programme, but sometimes another solution is needed. In contrast to po files, a more flexible approach is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. The key-value approach allows easier handling of the translated messages.

This package provides a collection of simple parameter estimation and tests for the comparison of multivariate means and variation, to accompany Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer (5th edition), by Manly BFJ, Navarro Alberto JA & Gerow K (2024) <doi:10.1201/9781003453482>.

This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using ShrinkShape to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. Note that all future developments will appear in this package and that PatternClass will eventually be archived.

Analysis of seed germination data using the physiological time modelling approach. Includes functions to fit hydrotime and thermal-time models with the traditional approaches of Bradford (1990) <doi:10.1104/pp.94.2.840> and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. Allows to fit models to grouped datasets, i.e. datasets containing multiple species, seedlots or experiments.

This package provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the MPFR libraries (<https://www.mpfr.org/>) which need to be installed separately (see description at <https://github.com/fuchslab/scModels>). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.

This package provides tools to help tag and validate data according to user-specified rules. The safeframe class adds variable level attributes to data.frame columns. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines clearer, more robust, and more reliable.

Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) <doi:10.1080/10618600.2023.2277875> as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2407.16299>. Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2504.02547>. Included are also complementary visualization and parameter tuning tools.

This package provides a dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) <doi:10.1111/2041-210X.13510> StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp.

This package provides methods for statistical disclosure control in tabular data such as primary and secondary cell suppression as described for example in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.

Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The skater package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.

It computes Relative survival, AER and SMR based on French death rates.

Extension to the spatstat package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression').

This package provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) <doi:10.1080/01621459.2014.957289>.

Extends the classical SSIM method proposed by Wang', Bovik', Sheikh', and Simoncelli'(2004) <doi:10.1109/TIP.2003.819861>. for irregular lattice-based maps and raster images. The geographical SSIM method incorporates well-developed geographically weighted summary statistics'('Brunsdon', Fotheringham and Charlton 2002) <doi:10.1016/S0198-9715(01)00009-6> with an adaptive bandwidth kernel function for irregular lattice-based maps.

The price action at any given time is determined by investor sentiment and market conditions. Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the quantmod package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).

Through simfinapi, you can intuitively access the SimFin Web-API (<https://www.simfin.com/>) to make SimFin data easily available in R. To obtain an SimFin API key (and thus to use this package), you need to register at <https://app.simfin.com/login>.

Assesses the number of concurrent users shiny applications are capable of supporting, and for directing application changes in order to support a higher number of users. Provides facilities for recording shiny application sessions, playing recorded sessions against a target server at load, and analyzing the resulting metrics.