Add a scroll back to top Font Awesome icon <https://fontawesome.com/> in rmarkdown documents and shiny apps thanks to jQuery GoTop <https://scottdorman.blog/jquery-gotop/>.

Create and maintain delayed-data packages (ddp's). Data stored in a ddp are available on demand, but do not take up memory until requested. You attach a ddp with g.data.attach(), then read from it and assign to it in a manner similar to S-PLUS, except that you must run g.data.save() to actually commit to disk.

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 provides a sparklyr <https://spark.rstudio.com/> extension that provides an R interface for GraphFrames <https://graphframes.github.io/>. GraphFrames is a package for Apache Spark that provides a DataFrame-based API for working with graphs. Functionality includes motif finding and common graph algorithms, such as PageRank and Breadth-first search.

Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.

Robust regression via gamma-divergence with L1, elastic net and ridge.

Computes probabilities related to group sequential designs for normally distributed test statistics. Enables to derive critical boundaries, power, drift, and confidence intervals of such designs. Supports the alpha spending approach by Lan-DeMets (1994) <doi:10.1002/sim.4780131308>.

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.

This package provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.

This package provides a ggplot2 extension that provides tools for automatically creating scales to focus on subgroups of the data plotted without losing other information.

Implementing generalized structured component analysis (GSCA) and its basic extensions, including constrained single and multiple group analysis, and second order latent variable modeling. For a comprehensive overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754).

Provision of classes and methods for estimating generalized orthogonal GARCH models. This is an alternative approach to CC-GARCH models in the context of multivariate volatility modeling.

Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

This package provides one-liner functions for common legend and guide operations in ggplot2'. Simplifies legend positioning, styling, wrapping, and collection across multi-panel plots created with patchwork or cowplot'.

Intended for both technical and non-technical users to create interactive data visualizations through a web browser GUI without writing any code.

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

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

Calculate different glucose variability measures, including average measures of glycemia, measures of glycemic variability and measures of glycemic risk, from continuous glucose monitoring data. Boris P. Kovatchev, Erik Otto, Daniel Cox, Linda Gonder-Frederick, and William Clarke (2006) <doi:10.2337/dc06-1085>. Jean-Pierre Le Floch, Philippe Escuyer, Eric Baudin, Dominique Baudon, and Leon Perlemuter (1990) <doi:10.2337/diacare.13.2.172>. C.M. McDonnell, S.M. Donath, S.I. Vidmar, G.A. Werther, and F.J. Cameron (2005) <doi:10.1089/dia.2005.7.253>. Everitt, Brian (1998) <doi:10.1111/j.1751-5823.2011.00149_2.x>. Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) <doi:10.2307/2234167>. Dougherty, R. L., Edelman, A. and Hyman, J. M. (1989) <doi:10.1090/S0025-5718-1989-0962209-1>. Tukey, J. W. (1977) <doi:10.1016/0377-2217(86)90209-2>. F. John Service (2013) <doi:10.2337/db12-1396>. Edmond A. Ryan, Tami Shandro, Kristy Green, Breay W. Paty, Peter A. Senior, David Bigam, A.M. James Shapiro, and Marie-Christine Vantyghem (2004) <doi:10.2337/diabetes.53.4.955>. F. John Service, George D. Molnar, John W. Rosevear, Eugene Ackerman, Leal C. Gatewood, William F. Taylor (1970) <doi:10.2337/diab.19.9.644>. Sarah E. Siegelaar, Frits Holleman, Joost B. L. Hoekstra, and J. Hans DeVries (2010) <doi:10.1210/er.2009-0021>. Gabor Marics, Zsofia Lendvai, Csaba Lodi, Levente Koncz, David Zakarias, Gyorgy Schuster, Borbala Mikos, Csaba Hermann, Attila J. Szabo, and Peter Toth-Heyn (2015) <doi:10.1186/s12938-015-0035-3>. Thomas Danne, Revital Nimri, Tadej Battelino, Richard M. Bergenstal, Kelly L. Close, J. Hans DeVries, SatishGarg, Lutz Heinemann, Irl Hirsch, Stephanie A. Amiel, Roy Beck, Emanuele Bosi, Bruce Buckingham, ClaudioCobelli, Eyal Dassau, Francis J. Doyle, Simon Heller, Roman Hovorka, Weiping Jia, Tim Jones, Olga Kordonouri,Boris Kovatchev, Aaron Kowalski, Lori Laffel, David Maahs, Helen R. Murphy, Kirsten Nørgaard, Christopher G.Parkin, Eric Renard, Banshi Saboo, Mauro Scharf, William V. Tamborlane, Stuart A. Weinzimer, and Moshe Phillip.International consensus on use of continuous glucose monitoring.Diabetes Care, 2017 <doi:10.2337/dc17-1600>.

Ease the transition between R vectors and markdown text. With gluedown and rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) <https://spec.commonmark.org/>.

This package implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. glmmfields uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with Stan'. References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.

This package creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the gimap package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <doi:10.1016/j.celrep.2021.109597>.

In computationally demanding data analysis pipelines, the targets R package (2021, <doi:10.21105/joss.02959>) maintains an up-to-date set of results while skipping tasks that do not need to rerun. This process increases speed and increases trust in the final end product. However, it also overwrites old output with new output, and past results disappear by default. To preserve historical output, the gittargets package captures version-controlled snapshots of the data store, and each snapshot links to the underlying commit of the source code. That way, when the user rolls back the code to a previous branch or commit, gittargets can recover the data contemporaneous with that commit so that all targets remain up to date.

GPU'/CPU Benchmarking on Debian-package based systems This package benchmarks performance of a few standard linear algebra operations (such as a matrix product and QR, SVD and LU decompositions) across a number of different BLAS libraries as well as a GPU implementation. To do so, it takes advantage of the ability to plug and play different BLAS implementations easily on a Debian and/or Ubuntu system. The current version supports - Reference BLAS ('refblas') which are un-accelerated as a baseline - Atlas which are tuned but typically configure single-threaded - Atlas39 which are tuned and configured for multi-threaded mode - Goto Blas which are accelerated and multi-threaded - Intel MKL which is a commercial accelerated and multithreaded version. As for GPU computing, we use the CRAN package - gputools For Goto Blas', the gotoblas2-helper script from the ISM in Tokyo can be used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.