This package provides a ggplot2 based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using ggplot2 techniques.

This package provides a comprehensive toolkit for scraping and analyzing book data from <https://www.goodreads.com/>. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from Goodreads data.

This is a wrapper for the command line tool googler', which can be found at the following URL: <https://github.com/jarun/googler>.

Second-order summary statistics K- and pair-correlation functions describe interactions in point pattern data. This package provides computations to estimate those statistics on inhomogeneous point processes, using the methods of in T Shaw, J Møller, R Waagepetersen, 2020 <doi:10.48550/arXiv.2004.00527>.

This package contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.

This package provides a way to log ggplot component calls, which can be useful for debugging and understanding how ggplot objects are created. The logged calls can be printed, saved, and re-executed to reproduce the original ggplot object.

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.

The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.

This package contains a function called gds() which accepts three input parameters like lower limits, upper limits and the frequencies of the corresponding classes. The gds() function calculate and return the values of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', gq2', gq3'), inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate effective data analysis. For skewness and kurtosis calculations we use moments.

Population-averaged models have been increasingly used in the design and analysis of cluster randomized trials (CRTs). To facilitate the applications of population-averaged models in CRTs, the package implements the generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by Li et al. (2022) <doi:10.1093/biostatistics/kxaa056> specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives a simple and efficient estimating equations approach based on the cluster-period means to estimate the intervention effects as well as correlation parameters. In addition, the package also provides functions for generating correlated binary data with specific mean vector and correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.

The Geocoordinate Validation Service (GVS) runs checks of coordinates in latitude/longitude format. It returns annotated coordinates with additional flags and metadata that can be used in data cleaning. Additionally, the package has functions related to attribution and metadata information. More information can be found at <https://github.com/ojalaquellueva/gvs/tree/master/api>.

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an R6 object and can be easily updated with new data. There are options to run in parallel, and Rcpp has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

Estimates the parameters of a GARCH-X model with exogenous covariates, performs hypothesis tests for the parameters returning the p-values, and uses False Discovery Rate p-value corrections to select the exogenous variables.

Sequential change-point tests, parameters estimation, and goodness-of-fit tests for generalized Ornstein-Uhlenbeck processes.

Wrapper around geom_histogram() of ggplot2 to plot the histogram of a numeric vector. This is especially useful, since qplot() was deprecated in ggplot2 3.4.0.

Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.

Likelihood-based boosting approaches for generalized mixed models are provided.

Access to The Guardian newspaper's open API <https://open-platform.theguardian.com/>, containing all articles published in The Guardian from 1999 to the present, including article text, metadata, tags and contributor information. An API key and registration is required.

GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting. The M in the name geeM is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R.

It can be necessary to limit the rate of execution of a loop or repeated function call e.g. to show or gather data only at particular intervals. This package includes two methods for limiting this execution rate; speed governors and timers. A speed governor will insert pauses during execution to meet a user-specified loop time. Timers are alarm clocks which will indicate whether a certain time has passed. These mechanisms are implemented in C to minimize processing overhead.

Computes probabilities related to group sequential designs for normally distributed test statistics. Enables to derive critical boundaries, power, drift, and confidence intervals of such designs. Supports the alpha spending approach by Lan-DeMets (1994) <doi:10.1002/sim.4780131308>.

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.

This package provides methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017).