This package provides a simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.

Defines classes and methods that can be used to implement genetic algorithms for feature selection. The idea is that we want to select a fixed number of features to combine into a linear classifier that can predict a binary outcome, and can use a genetic algorithm heuristically to select an optimal set of features.

Fast algorithms for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.

Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.

Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects.

Identifies implausible anthropometric (e.g., height, weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.

This package provides functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

Wrappers for functions in the gRain package to emulate some RHugin functionality, allowing the building of Bayesian networks consisting on discrete chance nodes incrementally, through adding nodes, edges and conditional probability tables, the setting of evidence, both hard (boolean) or soft (likelihoods), querying marginal probabilities and normalizing constants, and generating sets of high-probability configurations. Computations will typically not be so fast as they are with RHugin', but this package should assist users without access to Hugin to use code written to use RHugin'.

We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".

This package provides functions to read in the geometry format under the Neuroimaging Informatics Technology Initiative ('NIfTI'), called GIFTI <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.

Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.

We propose two distribution-free test statistics based on between-sample edge counts and measure the degree of relevance by standardized counts. Users can set edge costs in the graph to compare the parameters of the distributions. Methods for comparing distributions are as described in: Xiaoping Shi (2021) <arXiv:2107.00728>.

This package provides a ggplot2 extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.

Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.

Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.

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 >>.

Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.

This package provides a compilation of tools to complete common tasks for studying gerrymandering. This focuses on the geographic tool side of common problems, such as linking different levels of spatial units or estimating how to break up units. Functions exist for creating redistricting-focused data for the US.

This package provides an extension to ggplot2 (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) <doi:10.1109/TVCG.2021.3073466>.

This package provides a model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.

Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.

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.

Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.

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.