This package contains the Global Charcoal database data. Data include charcoal series (age, depth, charcoal quantity, associated units and methods) and information on sedimentary sites (localisation, depositional environment, biome, etc.) as well as publications informations. Since 4.0.0 the GCD mirrors the online SQL database at <http://paleofire.org>.

Simple interface to query gitignore.io to fetch gitignore templates that can be included in the .gitignore file. More than 450 templates are currently available.

Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.

This package provides a multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.

Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) <arXiv:2211.02314>.

It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>. The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex spatial patterns and large datasets with multiple predictor variables.

Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian two Poisson samples model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) <doi:10.1214/11-BJPS139>, Hamza & Vallois (2016) <doi:10.1016/j.spl.2016.03.014>, Chen & Novick (1984) <doi:10.3102/10769986009002163>.

Datasets used in the book Graphical Data Analysis with R (Antony Unwin, CRC Press 2015).

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

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>.

This package provides tools for geometric morphometric analyses and multidimensional data. Implements methods for morphological disparity analysis using bootstrap and rarefaction, as reviewed in Foote (1997) <doi:10.1146/annurev.ecolsys.28.1.129>. Includes integration and modularity testing, following Fruciano et al. (2013) <doi:10.1371/journal.pone.0069376>, using Escoufier's RV coefficient as test statistic as well as two-block partial least squares - PLS, Rohlf and Corti (2000) <doi:10.1080/106351500750049806>. Also includes vector angle comparisons, orthogonal projection for data correction (Burnaby (1966) <doi:10.2307/2528217>; Fruciano (2016) <doi:10.1007/s00427-016-0537-4>), and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992) <doi:10.1207/s15327906mbr2704_2>).

This package provides functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

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).

Simplifies the process of creating essential visualizations in R, offering a range of plotting functions for common chart types like violin plots, pie charts, and histograms. With an intuitive interface, users can effortlessly customize colors, labels, and styles, making it an ideal tool for both beginners and experienced data analysts. Whether exploring datasets or producing quick visual summaries, this package provides a streamlined solution for fundamental graphics in R.

Intended for both technical and non-technical users to create interactive data visualizations through a web browser GUI without writing any code.

Utilities to cost and evaluate Australian tax policy, including fast projections of personal income tax collections, high-performance tax and transfer calculators, and an interface to common indices from the Australian Bureau of Statistics. Written to support Grattan Institute's Australian Perspectives program, and related projects. Access to the Australian Taxation Office's sample files of personal income tax returns is assumed.

This package provides a ggplot2 geom and position for visualizing brain region data on cortical, subcortical, and white matter tract atlases. Brain atlas geometries are stored as simple features ('sf'), enabling seamless integration with the ggplot2 ecosystem including faceting, custom scales, and themes. Mowinckel & Vidal-Piñeiro (2020) <doi:10.1177/2515245920928009>.

This is a GitHub API wrapper for R. <https://docs.github.com/en/rest> It uses the gh package but has things wrapped up for convenient use cases.

To calculate the relative risk (RR) for the generalized additive model.

Offers various swiss maps as data frames and ggplot2 objects and gives the possibility to add layers of data on the maps. Data are publicly available from the swiss federal statistical office. In addition to the \codemaps2 object (a list of 8 swiss maps, at various levels), there are the data frames with the boundaries used to produce these maps (\codeshp_df, a list with 8 data frames).

Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.

This package provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

This package contains the development of a tool that provides a web-based graphical user interface (GUI) to perform Techniques from a subset of spatial statistics known as geographically weighted (GW) models. Contains methods described by Brunsdon et al., 1996 <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002 <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011 <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007 <doi:10.1111/j.1538-4632.2007.00709.x>.

Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions.