Computes and plots prediction intervals for numerical data or prediction sets for categorical data using prior information. Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) <arXiv:2204.08122> "Optimal Conformal Prediction for Small Areas".

This package provides a generative art system for producing tree-like images using an L-system to create the structures. The package includes tools for generating the data structures and visualise them in a variety of styles.

Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) <doi:10.1007/s11538-012-9749-6>.

Allows the user to implement easily canvas elements within a shiny app or an RMarkdown document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the fabricjs JavaScript library. See <http://fabricjs.com/>.

General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

This package provides an efficient C++ code for computing an optimal segmentation model with Poisson loss, up-down constraints, and label constraints, as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.

This package provides easy-to-understand and consistent interfaces for accessing data on the U.S. Congress. The functions in filibustr streamline the process for importing data on Congress into R, removing the need to download and work from CSV files and the like. Data sources include Voteview (<https://voteview.com/>), the U.S. Senate website (<https://www.senate.gov/>), and more.

An interface to the core Familias functions which are programmed in C++. The implementation is described in Egeland, Mostad and Olaisen (1997) <doi:10.1016/S1355-0306(97)72202-0> and Simonsson and Mostad (2016) <doi:10.1016/j.fsigen.2016.04.005>.

Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources.

Use R as a minimal build system. This might come in handy if you are developing R packages and can not use a proper build system. Stay away if you can (use a proper build system).

Adds flow maps to ggplot2 plots. The flow maps consist of ggplot2 layers which visualize the nodes as circles and the bilateral flows between the nodes as bidirectional half-arrows.

This package provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the fcm package returns a data frame of the concepts values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.

This package creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the fixest package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.

This package provides a tool to explore wide data sets, by detecting, ranking and plotting groups of statistically dependent columns.

For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis identity for the observed information matrix. Best-subset variable selection is then implemented since it becomes more feasible from the computational point of view.

This package provides a collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.

Allows prophet models from the prophet package to be used in a tidy workflow with the modelling interface of fabletools'. This extends prophet to provide enhanced model specification and management, performance evaluation methods, and model combination tools.

Calculation and plotting of instantaneous unavailabilities of basic events along with the top event of fault trees are issues important in reliability analysis of complex systems. Here, a fault tree is provided in terms of its minimal cut sets, along with reliability and maintainability distribution functions of the basic events. All the methods are derived from Horton (2002, ISBN: 3-936150-21-4), Niloofar and Lazarova-Molnar (2022).

Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ...

Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

Create Frequently Asked Questions page for Shiny application.

Fit a fractional binomial regression model and extended zero-inflated negative binomial regression model to count data with excess zeros using maximum likelihood estimation. Compare zero-inflated regression models via Vuong closeness test.

Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.