Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as coding and writing assistants for R users. Through these models, GenAI offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid R users in streamlining laborious coding and language processing tasks.

This package provides tools for solving common geocaching puzzle types, and other Geocaching-related tasks.

Seamless integration between R and Goose AI capabilities including memory management, visualization enhancements, and workflow automation. Save R objects to Goose memory, apply Block branding to visualizations, and manage data science project workflows. For more information about Goose AI, see <https://github.com/block/goose>.

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>.

This package provides a collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.

Two-Step Lasso (TS-Lasso) and compound minimum methods to recover the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an imputation method that handles various types of missing peaks simultaneously. This package provides the procedure to generate missing peaks (or data) for simulation study, as well as a tool to estimate and visualize the proportion of missing at random.

This function performs genomic prediction of cross performance using genotype and phenotype data. It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data, fitting mixed models, and predicting cross performance based on weighted marker effects. For more information, see Labroo et al. (2023) <doi:10.1007/s00122-023-04377-z>.

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.

Gitea is a community managed, lightweight code hosting solution were projects and their respective git repositories can be managed <https://gitea.io>. This package gives an interface to the Gitea API to access and manage repositories, issues and organizations directly in R.

Analysis of complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. Homogeneity of variances is checked using Cochran's C test and a posteriori comparisons of means are done using Student-Newman-Keuls (SNK) procedure. For those terms with no denominator in the F-ratio calculation, pooled mean squares and quasi F-ratios are provided. Magnitute of effects are assessed by components of variation.

Generate commonly used plots in the field of design of experiments using ggplot2'. ggDoE currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.

Geoms for placing arrowheads at multiple points along a segment, not just at the end; position function to shift starts and ends of arrows to avoid exactly intersecting points.

Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length and curvature).

Supports image files and graphic objects to be visualized in ggplot2 graphic system.

An S3 class groupedHyperframe that inherits from hyper data frame. Batch processes on point-pattern hyper column. Aggregation of function-value-table hyper column(s) and numeric hyper column(s) over a nested grouping structure.

Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.

Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>.

Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format.

This package provides statistical methods to check if a parametric family of conditional density functions fits to some given dataset of covariates and response variables. Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>, Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method.

This package provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.

It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>. The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex spatial patterns and large datasets with multiple predictor variables.

Implement some models for correlation/covariance matrices including two approaches to model correlation matrices from a graphical structure. One use latent parent variables as proposed in Sterrantino et. al. (2024) <doi:10.48550/arXiv.2312.06289>. The other uses a graph to specify conditional relations between the variables. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. In the first approach a natural sequence of simpler models along with a complexity penalization is used. The second penalizes deviations from a base model. These can be used as prior for model parameters, considering C code through the cgeneric interface for the INLA package (<https://www.r-inla.org>). This allows one to use these models as building blocks combined and to other latent Gaussian models in order to build complex data models.

Simplify ggplot2 visualisation with ggblanket wrapper functions.