The goal of gsDesign2 is to enable fixed or group sequential design under non-proportional hazards. To enable highly flexible enrollment, time-to-event and time-to-dropout assumptions, gsDesign2 offers piecewise constant enrollment, failure rates, and dropout rates for a stratified population. This package includes three methods for designs: average hazard ratio, weighted logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and MaxCombo tests. Substantial flexibility on top of what is in the gsDesign package is intended for selecting boundaries.

Estimation of the variogram through trimmed mean, radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, pocket plot, and design of optimal sampling networks through sequential and simultaneous points methods.

Selected utilities, in particular geoms and stats functions, extending the ggplot2 package. This package imports functions from EnvStats <doi:10.1007/978-1-4614-8456-1> by Millard (2013), ggpp <https://CRAN.R-project.org/package=ggpp> by Aphalo et al. (2023) and ggstats <doi:10.5281/zenodo.10183964> by Larmarange (2023), and then exports them. This package also contains modified code from ggquickeda <https://CRAN.R-project.org/package=ggquickeda> by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably.

Design of group sequential trials, including non-binding futility analysis at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).

This package provides tools for plotting gene clusters and transcripts by importing data from GenBank, FASTA, and GFF files. It performs BLASTP and MUMmer alignments [Altschul et al. (1990) <doi:10.1016/S0022-2836(05)80360-2>; Delcher et al. (1999) <doi:10.1093/nar/27.11.2369>] and displays results on gene arrow maps. Extensive customization options are available, including legends, labels, annotations, scales, colors, tooltips, and more.

This package provides a native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.

Interact with Google's Cloud Natural Language API <https://cloud.google.com/natural-language/> (v1) via R. The API has four main features, all of which are available through this R package: syntax analysis and part-of-speech tagging, entity analysis, sentiment analysis, and language identification.

This package implements cost-benefit analysis primitives from HM Treasury Green Book guidance (HM Treasury, 2022, 2026): the kinked Social Time Preference Rate (STPR), discount factors, net present value (NPV), equivalent annual cost, and real-terms rebasing using the GDP deflator. Designed for UK central government appraisal and evaluation. Bundled parameter tables carry vintage metadata for reproducibility.

Server implementation of GraphQL <http://spec.graphql.org/>, a query language originally created by Facebook for describing data requirements on complex application data models. Visit <https://graphql.org> to learn more about GraphQL'.

This package provides a post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) <http://www.statmod.org/workshops_archive_proceedings_2018.html>.

Plot brain atlases as interactive 3D meshes using Three.js via htmlwidgets', or render publication-quality static images through rgl and rayshader'. A pipe-friendly API lets you map data onto brain regions, control camera angles, toggle region edges, overlay glass brains, and snapshot or ray-trace the result. Additional atlases are available through the ggsegverse r-universe. Mowinckel & Vidal-Piñeiro (2020) <doi:10.1177/2515245920928009>.

An efficient algorithm to generate group assignments for classroom settings while minimizing repeated pairings across multiple rounds.

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 a grammar of graphics approach for visualizing summary statistics from multiple Genome-wide Association Studies (GWAS). It offers geneticists, bioinformaticians, and researchers a powerful yet flexible tool for illustrating complex genetic associations using data from various GWAS datasets. The visualizations can be extensively customized, facilitating detailed comparative analysis across different genetic studies. Reference: Uffelmann, E. et al. (2021) <doi:10.1038/s43586-021-00056-9>.

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

This package provides a set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas TraMineR uses base R to produce the plots this library draws on ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class ggplot', i.e. components can be added and tweaked using + and regular ggplot2 functions.

Add vector field layers to ggplots. Ideal for visualising wind speeds, water currents, electric/magnetic fields, etc. Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars) objects as input. Vector fields are depicted as arrows starting at specified locations, and with specified angles and radii.

This package provides a ggplot2 based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using ggplot2 techniques.

Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects.

This package implements Goldilocks adaptive trial designs for time-to-event and fixed-time binary endpoints. Outcomes are generated with a piecewise exponential model, with conjugate Gamma priors used for predictive imputation. Final analyses may use log-rank, Cox, or restricted mean survival time tests, Bayesian piecewise-exponential inference, frequentist risk differences, or Bayesian beta-binomial inference. The method closely follows Broglio and colleagues (2014) <doi:10.1080/10543406.2014.888569> and supports simulation of design operating characteristics.

Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127 for more details.

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

Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) <doi:10.1007/s11004-022-10036-8>. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) <doi:10.1080/19475683.2018.1534890>. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.

Fits explanatory generalized partial credit models and related ordinal item response models with global and item-specific covariate effects. Penalized marginal maximum likelihood estimation is used for variable selection, detection of differential item functioning, and clustering of item-specific covariate effects by fusion penalties. The package extends the regularization approach for differential item functioning in generalized partial credit models proposed by Schauberger and Mair (2020) <doi:10.3758/s13428-019-01224-2>.