Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where the likelihood is maximized by several optimization procedures using the GNU Scientific Library (GSL)', translated to C++ code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions.

This package creates reports from Trello, a collaborative, project organization and list-making application. <https://trello.com/> Reports are created by comparing individual Trello board cards from two different points in time and documenting any changes made to the cards.

Perform a regression analysis, generate a regression table, create a scatter plot, and download the results. It uses stargazer for generating regression tables and ggplot2 for creating plots. With just two lines of code, you can perform a regression analysis, visualize the results, and save the output. It is part of my make R easy project where one doesn't need to know how to use various packages in order to get results and makes it easily accessible to beginners. This is a part of my make R easy project. Help from ChatGPT was taken. References were Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Make your phrase or sentence into something funny! Pass a string with the keywords in, and get out a bit of humor.

This package provides a method for fitting the entire regularization path of the reluctant generalized additive model (RGAM) for linear regression, logistic, Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019) <arXiv:1912.01808> for details.

This package implements the rquery piped Codd-style query algebra using data.table'. This allows for a high-speed in memory implementation of Codd-style data manipulation tools.

This package provides tools for simulating synthetic survival data using a variety of methods, including kernel density estimation, parametric distribution fitting, and bootstrap resampling techniques for a desired sample size.

Easily download datasets from Kaggle <https://www.kaggle.com/> directly into your R environment using RKaggle'. Streamline your data analysis workflows by importing datasets effortlessly and focusing on insights rather than manual data handling. Perfect for data enthusiasts and professionals looking to integrate Kaggle datasets into their R projects with minimal hassle.

An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.

Quickly install Java Development Kit (JDK) without administrative privileges and set environment variables in current R session or project to solve common issues with Java environment management in R'. Recommended to users of Java'/'rJava'-dependent R packages such as r5r', opentripplanner', xlsx', openNLP', rWeka', RJDBC', tabulapdf', and many more. rJavaEnv prevents common problems like Java not found, Java version conflicts, missing Java installations, and the inability to install Java due to lack of administrative privileges. rJavaEnv automates the download, installation, and setup of the Java on a per-project basis by setting the relevant JAVA_HOME in the current R session or the current working directory (via .Rprofile', with the user's consent). Similar to what renv does for R packages, rJavaEnv allows different Java versions to be used across different projects, but can also be configured to allow multiple versions within the same project (e.g. with the help of targets package). Note: there are a few extra steps for Linux users, who don't have any Java previously installed in their system, and who prefer package installation from source, rather then installing binaries from Posit Package Manager'. See documentation for details.

Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website.

An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.

This package provides tools for optimal subset matching of treated units and control units in observational studies, with support for refined covariate balance constraints, (including fine and near-fine balance as special cases). A close relative is the rcbalance package. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel and Kelz (2020) <doi:10.1080/01621459.2020.1720693>. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).

Handle JSON-stat format (<https://json-stat.org>) in R. Not all features are supported, especially the extensive metadata features of JSON-stat'.

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). It calculates the final sorted rankings based on a decision matrix where rows represent alternatives and columns represent criteria. The method uses: - A numeric vector of weights for each criterion (the sum of weights must be 1). - A numeric vector of ideal values for each criterion. - A numeric vector of anti-ideal values for each criterion. - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, and the extent to which the anti-ideal value is considered worse. The function standardizes the decision matrix, normalizes the data, applies weights, and returns the final sorted rankings.

Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).

Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.

The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. <DOI: 10.1080/00273170802285693>. <DOI: 10.1037/a0025697>.

Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.

This package provides a set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data.

Allow for easy-to-use testing or evaluating of linear equality and inequality restrictions about parameters and effects in (generalized) linear statistical models.

This package provides a GUI front-end for ggplot2 supports Kaplan-Meier plot, histogram, Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart, bar chart, contour plot, and distribution plot.

This package contains example data for the rehh package.