Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 <doi:10.5753/sbbd.2021.17891>).

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.

Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.

This package provides tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. The user provides a data frame with the subject's covariate data and the information about the binary dosage file returned by the BinaryDosage::getbdinfo() routine.

This package provides a convenient R interface to the Genotype-Tissue Expression (GTEx) Portal API. The GTEx project is a comprehensive public resource for studying tissue-specific gene expression and regulation in human tissues. Through systematic analysis of RNA sequencing data from 54 non-diseased tissue sites across nearly 1000 individuals, GTEx provides crucial insights into the relationship between genetic variation and gene expression. This data is accessible through the GTEx Portal API enabling programmatic access to human gene expression data. For more information on the API, see <https://gtexportal.org/api/v2/redoc>.

This package implements a geographically weighted partial correlation which is an extension from gwss() function in the GWmodel package (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).

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.

Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a circular genome according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user. Cabrera et al (2012) <doi:10.1534/g3.112.002618> .

This package provides a gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.

Fit a regression model for when the response variable is presented as a ratio or proportion. This adjustment can occur globally, with the same estimate for the entire study space, or locally, where a beta regression model is fitted for each region, considering only influential locations for that area. Da Silva, A. R. and Lima, A. O. (2017) <doi:10.1016/j.spasta.2017.07.011>.

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.

This package implements univariate gamma frailty regression models for survival data with six different baseline distributions: the Arvind distribution (Pandey et al., 2024), the Lindley distribution (Lindley, 1958), the Linear Failure Rate distribution (Bain, 1974), the Power Xgamma distribution (Tyagi et al., 2022), the Modified Topp-Leone distribution (Singh et al., 2025), and the Power Failure Rate distribution (Mugdadi, 2005). The package supports uncensored (complete) and censored data (right, left, interval, and progressive censoring) with and without covariates. It provides maximum likelihood estimation, standard errors, confidence intervals, t-statistics, p-values, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), a bootstrap approximation of the Widely Applicable Information Criterion (WAIC), k-fold cross-validation, variance inflation factors, R-squared, adjusted R-squared, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), an overall model F-test, frailty variance estimation, survival probabilities at user-specified time points, median survival, expected survival within a fixed window, risk predictions, marginal predictions, martingale and deviance residuals, standardized and studentized residuals, leverage values, Cook's distance, Difference in Fits (DFFITS), Difference in Betas (DFBETAS), and a comprehensive suite of diagnostic and survival plots including Kaplan-Meier overlays and coefficient forest plots. Random number generation is available for each baseline distribution and the full frailty model, and a simulation study function evaluates parameter recovery across sample sizes and censoring scenarios. References are Lindley (1958) <doi:10.1111/j.2517-6161.1958.tb00278.x>, Mugdadi (2005) <doi:10.1016/j.amc.2004.09.064>, Bain (1974) <doi:10.1080/00401706.1974.10489237>, Singh, Tyagi, Singh, and Tyagi (2025) <https://ph02.tci-thaijo.org/index.php/thaistat/article/view/257215>, Pandey, Singh, Tyagi, and Tyagi (2024) <https://ssca.org.in/journal.html>, and Tyagi, Kumar, Pandey, Saha, and Bagariya (2022) <https://ijsreg.com/>.

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 collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.

This package provides a quantile-quantile plot can be used to compare a sample of p-values to the uniform distribution. But when the dataset is big (i.e. > 1e4 p-values), plotting the quantile-quantile plot can be slow. geom_QQ uses all the data to calculate the quantiles, but thins it out in a way that focuses on points near zero before plotting to speed up plotting and decrease file size, when vector graphics are stored.

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.

Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL <https://oops.uni-oldenburg.de/id/eprint/4290>').

This package provides a series of aliases to commonly used but difficult to remember ggplot2 sequences.

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

Enables users to create simple plots of biological culture plates as well as microplates. Both continuous and discrete values can be plotted onto the plate layout.

This package implements the Generalization Error Minimization in SubSampling (GEMSS) algorithm for sequential subdata selection in large-scale Gaussian process modeling (Chang, Hua, and Wu, 2026) <doi:10.1080/00401706.2026.2670596>. The method selects data points by a criterion consisting of predictive and space-filling parts, enabling efficient surrogate modeling for massive datasets.

This package implements the GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601> , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs) as base classifiers. Once an ensemble classifier has been trained, it can be used for predictions on new data. A function for cross validation is also included.

This package performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.

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