This package provides a ggplot2'-consistent approach to generating 2D displays of volumetric brain imaging data. Display data from multiple NIfTI images using standard ggplot2 conventions such scales, limits, and themes to control the appearance of displays. The resulting plots are returned as patchwork objects, inheriting from ggplot', allowing for any standard modifications of display aesthetics supported by ggplot2'.

Collection of packages for work with API Google Ads <https://developers.google.com/google-ads/api/docs/start>, Yandex Direct <https://yandex.ru/dev/direct/>, Yandex Metrica <https://yandex.ru/dev/metrika/>, MyTarget <https://target.my.com/help/advertisers/api_arrangement/ru>, Vkontakte <https://vk.com/dev/methods>, Facebook <https://developers.facebook.com/docs/marketing-apis/> and AppsFlyer <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>. This packages allows you loading data from ads account and manage your ads materials.

Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package GISINTEGRATION offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

Create tibbles and lists of ggplot figures that can be modified as easily as regular ggplot figures. Typical use cases are for creating reports or web pages where many figures are needed with different data and similar formatting.

This package provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300> together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>. Casiraghi, G., (2017) <arXiv:1702.02048> Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926> Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>. Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>. Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.

Bindings to the libgraphqlparser C++ library. Parses GraphQL <https://graphql.org> syntax and exports the AST in JSON format.

This package provides tools for using genetic markers, stable isotope data, and habitat suitability data to calculate posterior probabilities of breeding origin of migrating birds.

Create a user-friendly plotting GUI for R'. In addition, one purpose of creating the R package is to facilitate third-party software to call R for drawing, for example, Phoenix WinNonlin software calls R to draw the drug concentration versus time curve.

Generalized competing event model based on Cox PH model and Fine-Gray model. This function is designed to develop optimized risk-stratification methods for competing risks data, such as described in: 1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F, and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>. 2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>. 3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.

This package provides extension types and conversions to between R-native object types and Arrow columnar types. This includes integration among the arrow', nanoarrow', sf', and wk packages such that spatial metadata is preserved wherever possible. Extension type implementations ensure first-class geometry data type support in the arrow and nanoarrow packages.

This package provides methods and tools for the analysis of Genome Wide Identity-by-Descent ('gwid') mapping data, focusing on testing whether there is a higher occurrence of Identity-By-Descent (IBD) segments around potential causal variants in cases compared to controls, which is crucial for identifying rare variants. To enhance its analytical power, gwid incorporates a Sliding Window Approach, allowing for the detection and analysis of signals from multiple Single Nucleotide Polymorphisms (SNPs).

This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.

An interactive document on the topic of goodness of fit analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

Implementation of spatial graph-theoretic genetic gravity models. The model framework is applicable for other types of spatial flow questions. Includes functions for constructing spatial graphs, sampling and summarizing associated raster variables and building unconstrained and singly constrained gravity models.

Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.

Utilizes methods of the PyMongo Python library to initialize, insert and query GeoJson data (see <https://github.com/mongodb/mongo-python-driver> for more information on PyMongo'). Furthermore, it allows the user to validate GeoJson objects and to use the console for MongoDB (bulk) commands. The reticulate package provides the R interface to Python modules, classes and functions.

This package provides a fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) <url:<https://projecteuclid.org/journalArticle/Download?urlId=10.1214%2F17-AOS1652 >>. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) <doi:10.48550/arXiv.2008.02904>. Reinhard Furrer and Roman Flury and Florian Gerber (2022) <url:<https://CRAN.R-project.org/package=spam >>.

An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.

Multidimensional systems allow complex queries to be carried out in an easy way. The geographical dimension, together with the temporal dimension, plays a fundamental role in multidimensional systems. Through this package, vector geographic data layers can be associated to the attributes of geographic dimensions, so that the results of multidimensional queries can be obtained directly as vector layers. The multidimensional structures on which we can define the queries can be created from a flat table or imported directly using functions from this package.

This package provides functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.

Using overlap grouped-lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. See <doi:10.48550/arXiv.1506.03850> for more details.

This package implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi:10.1111/biom.13321>). Both singly robust and doubly robust ICE estimators based on parametric models are available. The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events. The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.

This package provides functions to load and analyze three open Electronic Health Records (EHRs) datasets of patients diagnosed with glioblastoma, previously released under the Creative Common Attribution 4.0 International (CC BY 4.0) license. Users can generate basic descriptive statistics, frequency tables and save descriptive summary tables, as well as create and export univariate or bivariate plots. The package is designed to work with the included datasets and to facilitate quick exploratory data analysis and reporting. More information about these three datasets of EHRs of patients with glioblastoma can be found in this article: Gabriel Cerono, Ombretta Melaiu, and Davide Chicco, Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March 2024). <doi:10.1007/s41666-023-00138-1>.