Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). This setup enables the modeling of a time series response variable using linear and nonlinear structures of a set of explanatory variables, with nonparametric components approximated by natural cubic splines or P-splines. It also accounts for autoregressive error terms with distributions that have lighter or heavier tails than the normal distribution. The package includes various error distributions, such as normal, generalized normal, Student's t, generalized Student's t, power-exponential, and Cauchy distributions. Chou-Chen, S.W., Oliveira, R.A., Raicher, I., Gilberto A. Paula (2024) <doi:10.1007/s00362-024-01590-w>.

Offers a set of functions to easily make predictions for univariate time series. autoTS is a wrapper of existing functions of the forecast and prophet packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

The functions in this package inspect, read, edit and run files for APSIM "Next Generation" ('JSON') and APSIM "Classic" ('XML'). The files with an apsim extension correspond to APSIM Classic (7.x) - Windows only - and the ones with an apsimx extension correspond to APSIM "Next Generation". For more information about APSIM see (<https://www.apsim.info/>) and for APSIM next generation (<https://apsimnextgeneration.netlify.app/>).

Align-GVGD ('A-GVGD') is a method to predict the impact of missense substitutions based on the properties of amino acid side chains and protein multiple sequence alignments <doi:10.1136/jmg.2005.033878>. A-GVGD is an extension of the original Grantham distance to multiple sequence alignments. This package provides an alternative R implementation to the web version found on <http://agvgd.hci.utah.edu/>.

Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), <doi:10.1044/policy.GL2005-00014>.

Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data. Krisanat Anukarnsakulchularp (2023) <https://github.com/KrisanatA/animbook-journal>.

This package provides functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, <doi:10.1080/10618600.2018.1513367> describes the concepts implemented.

An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.

This package implements a web-based graphics device for animated visualisations. Modelled on the base syntax, it extends the base graphics functions to support frame-by-frame animation and keyframes animation. The target use cases are real-time animated visualisations, including agent-based models, dynamical systems, and animated diagrams. The generated visualisations can be deployed as GIF images / MP4 videos, as Shiny apps (with interactivity) or as HTML documents through embedding into R Markdown documents.

Spatial modeling of energy balance and actual evapotranspiration using satellite images and meteorological data. Options of satellite are: Landsat-8 (with and without thermal bands), Sentinel-2 and MODIS. Respectively spatial resolutions are 30, 100, 10 and 250 meters. User can use data from a single meteorological station or a grid of meteorological stations (using any spatial interpolation method). Silva, Teixeira, and Manzione (2019) <doi:10.1016/j.envsoft.2019.104497>.

This package provides non-invasive annotation of package load calls such as \codelibrary(), \codep_load(), and \coderequire() so that we can have an idea of what the packages we are loading are meant for.

This package provides tools for simulating data generated by direct observation recording. Behavior streams are simulated based on an alternating renewal process, given specified distributions of event durations and interim times. Different procedures for recording data can then be applied to the simulated behavior streams. Functions are provided for the following recording methods: continuous duration recording, event counting, momentary time sampling, partial interval recording, whole interval recording, and augmented interval recording.

This package implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint <doi:10.48550/arXiv.2402.00584>.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

Generates data for challenging machine learning models in Arena <https://arena.drwhy.ai> - an interactive web application. You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable Arena URL.

Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.

This is a simple and powerful package to create, render, preview, and deploy documentation websites for R packages. It is a lightweight and flexible alternative to pkgdown', with support for many documentation generators, including Quarto', Docute', Docsify', and MkDocs'.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).

Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform ANOVA with post-hoc tests (e.g. Tukey HSD and Duncan MRR), compute coefficients of variation, and generate publication-ready summaries. High-level wrappers allow automated multi-variable analysis with optional clustering by experimental factors, as well as direct export of results to Excel spreadsheets and high-resolution image tables for reporting. Functions build on ggplot2', stats', and related packages and follow methods widely used in agronomy (field trials and plant breeding). Key references include Tukey (1949) <doi:10.2307/3001913>, Duncan (1955) <doi:10.2307/3001478>, and Cohen (1988, ISBN:9781138892899); see also agricolae <https://CRAN.R-project.org/package=agricolae> and Wickham (2016, ISBN:9783319242750> for ggplot2'. Versión en español: Conjunto de herramientas para el análisis estadà stico, visualización y generación de reportes en ensayos agroindustriales y agrà colas. Incluye funciones para ANOVA con pruebas post-hoc, resúmenes automáticos multivariables con o sin agrupamiento por factores, y exportación directa de resultados a Excel e imágenes de alta resolución para informes técnicos.

Browse through a continuously updated list of existing RStudio addins and install/uninstall their corresponding packages.

The goal is to print an "aperçu", a short view of a vector, a matrix, a data.frame, a list or an array. By default, it prints the first 5 elements of each dimension. By default, the number of columns is equal to the number of lines. If you want to control the selection of the elements, you can pass a list, with each element being a vector giving the selection for each dimension.

Leveraging Monte Carlo simulations, this package provides tools for diagnosing regression models. It implements a parametric bootstrap framework to compute statistics, generates diagnostic envelopes to assess goodness-of-fit, and evaluates type I error control for Wald tests. By simulating data under the assumption that the model is true, it helps to identify model mis-specifications and enhances the reliability of the model inferences.

This package provides a unified and straightforward interface for performing a variety of meta-analysis methods directly from user data. Users can input a data frame, specify key parameters, and effortlessly execute and compare multiple common meta-analytic models. Designed for immediate usability, the package facilitates transparent, reproducible research without manual implementation of each analytical method. Ideal for researchers aiming for efficiency and reproducibility, it streamlines workflows from data preparation to results interpretation.

This package provides a novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.