This package provides a collection of tools to deal with raster maps.

This package provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a ggplot object, allowing the user to further customize the output. This package is part of the R4Epis project <https://r4epis.netlify.app/>.

This package contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure.

Confidence curves, confidence intervals and p-values for correlation coefficients corrected for attenuation due to measurement error. Implements the methods described in Moss (2019, <arxiv:1911.01576>).

Create beautiful and interactive visualizations in a single function call. The data.table package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).

Import, manipulate and explore results generated by Antares', a powerful open source software developed by RTE (Réseau de Transport dâ à lectricité) to simulate and study electric power systems (more information about Antares here : <https://antares-simulator.org/>).

This package provides functions for interacting directly with the ALTADATA API. With this R package, developers can build applications around the ALTADATA API without having to deal with accessing and managing requests and responses. ALTADATA is a curated data marketplace for more information go to <https://www.altadata.io>.

Created to host raw accelerometry data sets and their derivatives which are used in the corresponding adept package.

This package provides WHO 2007 References for School-age Children and Adolescents (5 to 19 years) (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/growth-reference-data-for-5to19-years>.

The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.

An interface for data processing, building models, predicting values and analysing outcomes. Fitting Linear Models, Robust Fitting of Linear Models, k-Nearest Neighbor Classification, 1-Nearest Neighbor Classification, and Conditional Inference Trees are available.

This package provides a few functions and several data set for the Springer book Applied Predictive Modeling'.

Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.

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'.

Animate Shiny and R Markdown content when it comes into view using animate-css effects thanks to jQuery AniView'.

This package provides a tidy framework for automatic knowledge classification and visualization. Currently, the core functionality of the framework is mainly supported by modularity-based clustering (community detection) in keyword co-occurrence network, and focuses on co-word analysis of bibliometric research. However, the designed functions in akc are general, and could be extended to solve other tasks in text mining as well.

This package provides a thin wrapper around the ajv JSON validation package for JavaScript. See <http://epoberezkin.github.io/ajv/> for details.

Terrain change detection, cut and fill volume estimation, terrain profiling, flood inundation analysis, slope and aspect computation, hillshade generation, contour extraction, reclamation monitoring, erosion analysis, and engineering export (LandXML, STL) from LiDAR (Light Detection and Ranging) point clouds and digital elevation models ('DEMs'). Applications include surface mine reclamation monitoring, sediment pond capacity tracking, highwall safety classification, and erosion channel detection. Built on lidR for point cloud I/O and terra for raster operations. Includes access utilities for KyFromAbove cloud-native elevation data on Amazon Web Services ('AWS') <https://kyfromabove.ky.gov/>. Methods for terrain change detection and volume estimation follow Li and others (2005) <doi:10.1016/j.geomorph.2004.10.007>.

Automatically calculates cognostic groups for plot objects and list column plot objects. Results are returned in a nested data frame.

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.

This package provides easy access to the AviList Global Avian Checklist, the first unified global bird taxonomy that harmonizes previous differences between International Ornithological Committee ('IOC'), Clements', and BirdLife checklists. This package contains the complete AviList dataset as R data objects ready for ornithological research and analysis. For more details see AviList Core Team (2025) <doi:10.2173/avilist.v2025>.

Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.

This package implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors.

Calculate ActiGraph counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in ActiGraph Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and python implementation of this code (<https://github.com/actigraph/agcounts>).