This package provides a variety of multivariable data summary statistics and constructions have been proposed, either to generalize univariable analogs or to exploit multivariable properties. Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) <doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. gggda extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data.

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

Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

This package provides functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.

It provides materials (i.e. serial axes objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.

Convert Ensembl gene identifiers from Genotype-Tissue Expression (GTEx) data to identifiers in other annotation systems, including Entrez', HGNC', and UniProt'.

This package contains methods for fitting Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). Generalized regression models are common methods for handling data for which assuming Gaussian-distributed errors is not appropriate. For instance, if the response of interest is binary, count, or proportion data, one can instead model the expectation of the response based on an appropriate data-generating distribution. This package provides methods for fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma regression, and Binomial regression (currently GLM only) settings. Models are fit using local scoring algorithms described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.

Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

This package provides a ggplot2 extension that adds specialised arrow geometry layers. It offers more arrow options than the standard grid arrows that are built-in many line-based geom layers.

Implement group response-adaptive randomization procedures, which also integrates standard non-group response-adaptive randomization methods as specialized instances. It is also uniquely capable of managing complex scenarios, including those with delayed and missing responses, thereby expanding its utility in real-world applications. This package offers 16 functions for simulating a variety of response adaptive randomization procedures. These functions are essential for guiding the selection of statistical methods in clinical trials, providing a flexible and effective approach to trial design. Some of the detailed methodologies and algorithms used in this package, please refer to the following references: LJ Wei (1979) <doi:10.1214/aos/1176344614> L. J. WEI and S. DURHAM (1978) <doi:10.1080/01621459.1978.10480109> Durham, S. D., FlournoY, N. AND LI, W. (1998) <doi:10.2307/3315771> Ivanova, A., Rosenberger, W. F., Durham, S. D. and Flournoy, N. (2000) <https://www.jstor.org/stable/25053121> Bai Z D, Hu F, Shen L. (2002) <doi:10.1006/jmva.2001.1987> Ivanova, A. (2003) <doi:10.1007/s001840200220> Hu, F., & Zhang, L. X. (2004) <doi:10.1214/aos/1079120137> Hu, F., & Rosenberger, W. F. (2006, ISBN:978-0-471-65396-7). Zhang, L. X., Chan, W. S., Cheung, S. H., & Hu, F. (2007) <https://www.jstor.org/stable/26432528> Zhang, L., & Rosenberger, W. F. (2006) <doi:10.1111/j.1541-0420.2005.00496.x> Hu, F., Zhang, L. X., Cheung, S. H., & Chan, W. S. (2008) <doi:10.1002/cjs.5550360404>.

This package provides functions for drawing scene trees representing scenes that have been drawn using grid graphics.

The first major functionality is to compute the bias in regression coefficients of misspecified linear gene-environment interaction models. The most generalized function for this objective is GE_bias(). However GE_bias() requires specification of many higher order moments of covariates in the model. If users are unsure about how to calculate/estimate these higher order moments, it may be easier to use GE_bias_normal_squaredmis(). This function places many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate normal distribution) and is thus able to automatically calculate many of the higher order moments automatically, necessitating only that the user specify some covariances. There are also functions to solve for the bias through simulation and non-linear equation solvers; these can be used to check your work. Second major functionality is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample performance than other inference procedures for testing GxE interaction. More details on these functions are available in Sun, Carroll, Christiani, and Lin (2018) <doi:10.1111/biom.12813>.

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

This package contains the framework of the estimation, sampling, and hypotheses testing for two special distributions (Exponentiated Exponential-Pareto and Exponentiated Inverse Gamma-Pareto) within the family of Generalized Exponentiated Composite distributions. The detailed explanation and the applications of these two distributions were introduced in Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.1080/03610926.2022.2050399>, Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/math10111895>, and Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/app13010645>.

Scrapes Google Citation pages and creates data frames of citations over time.

New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2022) <arXiv:2205.13787>.

Splits date and time of day components from continuous datetime objects, then plots them using grammar of graphics ('ggplot2'). Plots can also be decorated with solar cycle information (e.g., sunset, sunrise, etc.). This is useful for visualising data that are associated with the solar cycle.

Power and sample size calculations for genetic association studies allowing for misspecification of the model of genetic susceptibility. "Hum Hered. 2019;84(6):256-271.<doi:10.1159/000508558>. Epub 2020 Jul 28." Power and/or sample size can be calculated for logistic (case/control study design) and linear (continuous phenotype) regression models, using additive, dominant, recessive or degree of freedom coding of the genetic covariate while assuming a true dominant, recessive or additive genetic effect. In addition, power and sample size calculations can be performed for gene by environment interactions. These methods are extensions of Gauderman (2002) <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973> and are described in: Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification. American Society of Human Genetics. October 2018, San Diego.

This is a dataset package for GANPA, which implements a network-based gene weighting approach to pathway analysis. This package includes data useful for GANPA, such as a functional association network, pathways, an expression dataset and multi-subunit proteins.

The function gggap() streamlines the creation of segments on the y-axis of ggplot2 plots which is otherwise not a trivial task to accomplish.

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

Calculates Agresti's generalized odds ratios. For a randomly selected pair of observations from two groups, calculates the odds that the second group will have a higher scoring outcome than that of the first group. Package provides hypothesis testing for if this odds ratio is significantly different to 1 (equal chance).

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.