Encodes simple feature ('sf') objects and coordinates, and decodes polylines using the Google polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).

Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the gets package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.

Testing, Implementation and Forecasting of Grey Model (GM(1, 1)). For method details see Hsu, L. and Wang, C. (2007). <doi:10.1016/j.techfore.2006.02.005>.

Selected utilities, in particular geoms and stats functions, extending the ggplot2 package. This package imports functions from EnvStats <doi:10.1007/978-1-4614-8456-1> by Millard (2013), ggpp <https://CRAN.R-project.org/package=ggpp> by Aphalo et al. (2023) and ggstats <doi:10.5281/zenodo.10183964> by Larmarange (2023), and then exports them. This package also contains modified code from ggquickeda <https://CRAN.R-project.org/package=ggquickeda> by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably.

Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) <doi:10.1002/mma.9823>.

Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.

Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.

Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.

This package creates bar plots with rounded corners using ggplot2'. The code in this package was adapted from a solution provided by Stack Overflow user sthoch in the following post <https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.

Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the GeoDa software workbook and data site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin and team at the University of Chicago. Datasets are stored as sf objects.

This package provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

Aligns peak based on peak retention times and matches homologous peaks across samples. The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 <doi:10.1371/journal.pone.0198311>.

Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects. Inputs are : data frame, RDA (package vegan') and PCA (package FactoMineR') objects. Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters.

Extends classical linear and quadratic discriminant analysis by incorporating permutation group symmetries into covariance matrix estimation. The package leverages methodology from the gips framework to identify and impose permutation structures that act as a form of regularization, improving stability and interpretability in settings with symmetric or exchangeable features. Several discriminant analysis variants are provided, including pooled and class-specific covariance models, as well as multi-class extensions with shared or independent symmetry structures. For more details about gips methodology see and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen, KoÅ odziejek (2025, <doi:10.18637/jss.v112.i07>).

Computing Global Sensitivity Indices from given data using Optimal Transport, as defined in Borgonovo et al (2024) <doi:10.1287/mnsc.2023.01796>. You provide an input sample, an output sample, decide the algorithm, and compute the indices.

This package provides a dataset about movies. This was previously contained in ggplot2, but has been moved its own package to reduce the download size of ggplot2.

An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with FAIR (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the DublinCore standard (<https://dublincore.org/>), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with ISO/TC211 (<https://committee.iso.org/home/tc211>) and OGC (<https://www.ogc.org/standards/>) geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. PostGIS'), metadata catalogues (eg. GeoNetwork', CSW servers), data servers (eg. GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of EML metadata, and their management with DataOne servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of CF conventions, NetCDF data format and OPeNDAP access protocol, and their management with Thredds servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', DataCite'), to facilitate the publication of data within (meta)data repositories such as Zenodo (<https://zenodo.org>) or DataVerse (<https://dataverse.org/>). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference Zenodo DOI in GeoNetwork'/'GeoServer metadata, or vice versa reference GeoNetwork'/'GeoServer links in Zenodo or EML metadata). The use of standardized configuration files ('JSON or YAML formats) allow fully reproducible workflows to facilitate the work of data and information managers.

Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

This package provides a function built on ggplot2 that visualizes pairwise BLAST alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences.

Fits the logistic equation to microbial growth curve data (e.g., repeated absorbance measurements taken from a plate reader over time). From this fit, a variety of metrics are provided, including the maximum growth rate, the doubling time, the carrying capacity, the area under the logistic curve, and the time to the inflection point. Method described in Sprouffske and Wagner (2016) <doi:10.1186/s12859-016-1016-7>.

This package provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) <doi:10.1089/jir.2017.0127>.

This package provides a method to predict and report gender from Brazilian first names using the Brazilian Institute of Geography and Statistics Census data.