This package provides convenient wrapper functions around the glue library for common string interpolation tasks. The package simplifies the process of combining glue string templating with common R functions like message(), warning(), stop(), print(), cat(), and file writing operations. Instead of manually calling glue() and then passing the result to these functions, glueDo provides direct wrapper functions that handle both steps in a single call. This is particularly useful for logging, error handling, and formatted output in R scripts and packages. The main reference for the underlying glue package is Hester and Bryan (2022) <https://CRAN.R-project.org/package=glue>.

The American Association Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma Collaborative represents a multi-year, multi-institution effort to build a pan-cancer repository of linked clinico-genomic data. The genomic and clinical data are provided in multiple releases (separate releases for each cancer cohort with updates following data corrections), which are stored on the data sharing platform Synapse <https://www.synapse.org/>. The genieBPC package provides a seamless way to obtain the data corresponding to each release from Synapse and to prepare datasets for analysis.

Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.

This package provides a ggplot2 extension that enables robust image grobs in panels and theme elements.

This package provides functions for matching student-answers to teacher answers for a variety of data types.

Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

This package provides a wrapper of different standard estimation methods for gravity models. This package provides estimation methods for log-log models and multiplicative models.

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

This package provides a network-based gene weighting algorithm for pathway enrichment analysis, using either RNA-seq or microarray data. Zhaoyuan Fang, Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.

Computes experimental designs for two-arm experiments with covariates using multiple methods, including: (0) complete randomization and randomization with forced-balance; (1) greedy optimization of a balance objective function via pairwise switching; (2) numerical optimization via gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5) exhaustive enumeration for small sample sizes; (6) binary pair matching using nbpMatching'; (7) binary pair matching plus method (1) to further optimize balance; (8) binary pair matching plus method (3) to further optimize balance; (9) Hadamard designs; and (10) simultaneous multiple kernels. For the greedy, rerandomization, and related methods, three objective functions are supported: Mahalanobis distance, standardized sums of absolute differences, and kernel distances via the kernlab library. This package is the result of a stream of research that can be found in Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random Designs with Greatly Improved Balance." Biometrika 106(3), 695-701 <doi:10.1093/biomet/asz026>. Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 <doi:10.1002/cjs.11685>.

This package provides efficient geospatial thinning algorithms to reduce the density of coordinate data while maintaining spatial relationships. Implements K-D Tree and brute-force distance-based thinning, as well as grid-based and precision-based thinning methods. For more information on the methods, see Elseberg et al. (2012) <https://hdl.handle.net/10446/86202>.

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

This package provides systematic, dependency-aware exploration of group sequential designs created with gsDesign'. Supports reproducible grid and random search over user-defined candidate sets, parallel evaluation via the future framework, standardized metric extraction, and auditable reporting for design-space evaluation and trade-off analysis. Methods for group sequential design are described in Anderson (2025) <doi:10.32614/CRAN.package.gsDesign>. The future framework for parallel processing is described in Bengtsson (2021) <doi:10.32614/RJ-2021-048>.

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

We implement two main functions. The first function uses a given grouped and/or right-censored grouping scheme and empirical data to infer parameters, and implements chi-square goodness-of-fit tests. The second function searches for the global optimal grouping scheme of grouped and/or right-censored count responses in surveys.

This package provides functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either ggplot2 or base R plotting.

Generalized correlation and related measures for assessing nonlinear relationships between variables, including mixed-type data. For methodological background, see <https://r-suzuki.github.io/gcor/method.html>.

Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on ggplot2 objects, performs the desired statistical test between groups of interest and annotates the test results on the plot.

Integer programming models to assign students to groups by maximising diversity within groups, or by maximising preference scores for topics.

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

This package provides tools for estimating forest metrics such as stem volume, biomass, and carbon using regional allometric equations. The package implements widely used models including Dagnelie P., Rondeux J. & Palm R. (2013, ISBN:9782870161258) "Cubage des arbres et des peuplements forestiers - Tables et equations" <https://orbi.uliege.be/handle/2268/155356>, Vallet P., Dhote J.-F., Le Moguedec G., Ravart M. & Pignard G. (2006) "Development of total aboveground volume equations for seven important forest tree species in France" <doi:10.1016/j.foreco.2006.03.013>, Pauwels D. & Rondeux J. (1999, ISSN:07779992) "Tarifs de cubage pour les petits bois de meleze (Larix sp.) en Ardenne" <https://orbi.uliege.be/handle/2268/96128>, Massenet J.-Y. (2006) "Chapitre IV: Estimation du volume" <https://jymassenet-foret.fr/cours/dendrometrie/Coursdendrometriepdf/Dendro4-2009.pdf>, France Valley (2025) "Bilan Carbone Forestier - Methodologie" <https://www.france-valley.com/hubfs/Bilan%20Carbone%20Forestier.pdf>. Its modular structure allows transparent integration of bibliographic or user-defined allometric relationships.

Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, Weibull', Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.

This package provides a path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.