Some extensions to Rcmdr (R Commander), randomness test, variance test for one normal sample and predictions using active model, made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).

Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground points filtering algorithm which is based on cloth simulation. It tries to simulate the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.

Three methods to calculate R2 for models with correlated errors, including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in Ives 2018 <doi:10.1093/sysbio/syy060>.

Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup.

The Refugee Population Statistics Database published by The Office of The United Nations High Commissioner for Refugees (UNHCR) contains information about forcibly displaced populations spanning more than 70 years of statistical activities. It covers displaced populations such as refugees, asylum-seekers and internally displaced people, including their demographics. Stateless people are also included, most of who have never been displaced. The database also reflects the different types of solutions for displaced populations such as repatriation or resettlement. More information on the data and methodology can be found on the UNHCR Refugee Data Finder <https://www.unhcr.org/refugee-statistics/>.

This package provides an R interface for using AmCharts Library. Based on htmlwidgets', it provides a global architecture to generate JavaScript source code for charts. Most of classes in the library have their equivalent in R with S4 classes; for those classes, not all properties have been referenced but can easily be added in the constructors. Complex properties (e.g. JavaScript object) can be passed as named list. See examples at <https://datastorm-open.github.io/introduction_ramcharts/> and <https://www.amcharts.com/> for more information about the library. The package includes the free version of AmCharts Library. Its only limitation is a small link to the web site displayed on your charts. If you enjoy this library, do not hesitate to refer to this page <https://www.amcharts.com/online-store/> to purchase a licence, and thus support its creators and get a period of Priority Support. See also <https://www.amcharts.com/about/> for more information about AmCharts company.

Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.

Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the BIC and the BDeu score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.

This package provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.

Direct insertion of over 1000 symbols (e.g. currencies, letters, emojis, arrows, mathematical symbols and so on) into Rmarkdown documents and Shiny applications by incorporating HTML hex codes.

This package provides a range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). Many of the RSurveillance functions are incorporated into the epitools website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.

Connect R with MOA (Massive Online Analysis - <https://moa.cms.waikato.ac.nz/>) to build classification models and regression models on streaming data or out-of-RAM data. Also streaming recommendation models are made available.

This package provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.

Calculate the matrices in Shiller (1991, <doi:10.1016/S1051-1377(05)80028-2>) that serve as the foundation for many repeat-sales price indexes.

Read and write las and laz binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://community.asprs.org/leadership-restricted/leadership-content/public-documents/standards>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.

Enhances the R Optimization Infrastructure ('ROI') package with a connection to the neos server. ROI optimization problems can be directly be sent to the neos server and solution obtained in the typical ROI style.

This package provides portable access from R to biomedical image processing toolbox ANTs by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless integration with the Python implementation ANTsPy'. Allows biomedical images to be processed in Python and analyzed in R', and vice versa via shared memory. See citation("rpyANTs") for more reference information.

Allows users to easily create references to R objects then dereference when needed or modify in place without using reference classes, environments, or active bindings as workarounds. Users can also create expression references that allow subsets of any object to be referenced or expressions containing references to multiple objects.

This package provides convenient tools for visualising ordinal outcome data following the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401>.

This package contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in Excel', creating date vectors and changing the console prompt to reflect time.

Linear and logistic ridge regression functions. Additionally includes special functions for genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.

Perform a supervised data analysis on a database through a shiny graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.