This package provides a Bayesian model selection approach for generalized linear mixed models. Currently, GLMMselect can be used for Poisson GLMM and Bernoulli GLMM. GLMMselect can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. GLMMselect can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. GLMMselect is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.

Streamline the creation of common charts by taking care of a lot of data preprocessing and plot customization for the user. Provides a high-level interface to create plots using ggplot2'.

This package provides geographical faceting functionality for ggplot2'. Geographical faceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation.

An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017)<doi:10.1553/giscience2017_01_s36> and are described in greater detail in (Tsutsumida et al., 2019)<doi:10.5194/ica-abs-1-372-2019> and (Percival et al., 2021)<arXiv:2101.03491>.

Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.

This package provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model.

This package implements an extension of ggplot2 (formerly ggESDA') and visualizes symbolic interval-valued data with various plots, offering more general and flexible input arguments. Additionally, it provides a function to transform classical data into symbolic data using both clustering algorithms and customized methods.

This package provides a function built on ggplot2 that visualizes pairwise BLAST alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences.

This package provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) <doi:10.48550/arXiv.2506.20425>.

Gene and Region Counting of Mutations (GARCOM) package computes mutation (or alleles) counts per gene per individuals based on gene annotation or genomic base pair boundaries. It comes with features to accept data formats in plink(.raw) and VCF. It provides users flexibility to extract and filter individuals, mutations and genes of interest.

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.

Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.

This package provides a pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) <doi:10.1101/436659> for more information and examples.

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.

Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.

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.

Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2025) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), multivariate regression (multigaussian), smoothed support vector machines (svm1), squared support vector machines (svm2), logistic regression (binomial), proportional odds logistic regression (ordinal), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.

Simulation tool to facilitate determination of required sample size to achieve category saturation for studies using multiple repertory grids in conjunction with content analysis.

This package provides a workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework. References: Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.

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> .

Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings.

This package provides a function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.

The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.

Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.