It analyzes raster maps and other information as input/output files from the Hydrological Distributed Model GEOtop. It contains functions and methods to import maps and other keywords from geotop.inpts file. Some examples with simulation cases of GEOtop 2.x/3.x are presented in the package. Any information about the GEOtop Distributed Hydrological Model can be found in the provided documentation.

Implementation of several goodness-of-fit tests for functional data. Currently, mostly related with the functional linear model with functional/scalar response and functional/scalar predictor. The package allows for the replication of the data applications considered in Garcà a-Portugués, à lvarez-Liébana, à lvarez-Pérez and González-Manteiga (2021) <doi:10.1111/sjos.12486>.

Interact with the Google Analytics APIs <https://developers.google.com/analytics/>, including the Core Reporting API (v3 and v4), Management API, User Activity API GA4's Data API and Admin API and Multi-Channel Funnel API.

Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076).

This package provides a coherent interface and implementation for creating grouped date classes.

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 implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

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>.

This package implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the randnet package <https://CRAN.R-project.org/package=randnet>. For more details, see the "proposed method" section of Liu, Bi, and Li (2025) <doi:10.48550/arXiv.2507.00402>.

This package provides methods for model selection, estimation, inference, and simulation for the multilevel factor model, based on the principal component estimation and generalised canonical correlation approach. Details can be found in "Generalised Canonical Correlation Estimation of the Multilevel Factor Model." Lin and Shin (2025) <doi:10.2139/ssrn.4295429>.

This package provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) <https://www3.stat.sinica.edu.tw/statistica/J12N1/J12N11/J12N11.html> and further extended by Wu, Tien, and Chen (2010) <doi:10.1016/j.csda.2008.09.029>.

Implemented are three Wald-type statistic and respective permuted versions for null hypotheses formulated in terms of cumulative hazard rate functions, medians and the concordance measure, respectively, in the general framework of survival factorial designs with possibly heterogeneous survival and/or censoring distributions, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Ditzhaus, Dobler and Pauly (2020) <doi:10.1177/0962280220980784> Ditzhaus, Janssen, Pauly (2020) <arXiv: 2004.10818v2> Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.

Preview what a ggplot2 plot would look like if you save it to a file. Attach picture dimensions as a canvas() element and get an instant preview. These dimensions will then be used when you save the plot.

This package provides a collection of functions for processing Gen5 2.06 exported data. Gen5 is an essential data analysis software for BioTek plate readers <https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, modeling and plotting using exported data from Gen5 version 2.06. It exports technically correct data defined in (Edwin de Jonge and Mark van der Loo (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It contains Boltzmann fitting for general kinetic analysis. See <https://www.github.com/yanxianUCSB/gen5helper> for more information, documentation and examples.

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

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.

Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using ggplot2'.

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.

This package provides a network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.

In computationally demanding data analysis pipelines, the targets R package (2021, <doi:10.21105/joss.02959>) maintains an up-to-date set of results while skipping tasks that do not need to rerun. This process increases speed and increases trust in the final end product. However, it also overwrites old output with new output, and past results disappear by default. To preserve historical output, the gittargets package captures version-controlled snapshots of the data store, and each snapshot links to the underlying commit of the source code. That way, when the user rolls back the code to a previous branch or commit, gittargets can recover the data contemporaneous with that commit so that all targets remain up to date.

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.

This package provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.

The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in our two papers. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2024) <doi:10.32614/RJ-2024-027>. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2023) <doi:10.1177/1471082X231219847>.