Convert English letters to numbers or numbers to English letters as on a telephone keypad. When converting letters to numbers, a character vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F" becoming 3, etc. When converting numbers to letters, a character vector is returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and "C").

It's my experience that working with shiny is intuitive once you're into it, but can be quite daunting at first. Several common mistakes are fairly predictable, and therefore we can control for these. The functions in this package help match up the assets listed in the UI and the SERVER files, and Visualize the ad hoc structure of the shiny App.

This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups.

ZToolkit (Ztk) is a cross-platform GUI toolkit heavily inspired by GTK. It handles events and low level drawing on behalf of the user and provides a high-level API for managing the UI and custom widgets. ZToolkit is written in C and was created to be used for building audio plugin UIs, where the dependencies often need to be kept to a minimum.

This package provides a unified parallelization framework for multiple backends. This package is designed for internal package and interactive usage. The main operation is parallel mapping over lists. It supports local, multicore, mpi and BatchJobs mode. It allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation.

This package implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics <doi:10.1063/1.2745299> (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia.

Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. Retrieves counternull sets and generates counternull distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. Package includes visualization of randomization distributions and Fisher Intervals. Users can input custom test statistics and their own methods for randomization. Rosenthal and Rubin (1994) <doi:10.1111/j.1467-9280.1994.tb00281.x>.

Create and integrate maps in your R workflow. This package helps to design cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title...), labels or legends. See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.

Builds the coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) <doi:10.1016/j.spl.2015.11.008>. This methodology studies the relationship between a couple of time series based on the the set of turning points of each time series. The coincident profile establishes if two time series are coincident, or one of them leads the second.

This package implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.

Several functions to compute indicators for organization and efficiency in visual foraging, multi-target visual search, and cancellation tasks. The current version of this package includes the following indicators: best-r, mean Inter-target Distance, Percentage Above Optimal (PAO) scan path, and intersections in the scan path. For more detailed descriptions, see Mark et al. (2004) <doi:10.1212/01.WNL.0000131947.08670.D4>.

Using the DNA sequence and gene annotation files provided in ENSEMBL <https://www.ensembl.org/index.html>, the functions implemented in the package try to find the DNA sequences and protein sequences of any given genomic loci, and to find the genomic coordinates and protein sequences of any given protein locations, which are the frequent tasks in the analysis of genomic and proteomic data.

An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. LipidomicsR simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species.

Datasets for nlmixr2 and rxode2'. nlmixr2 is used for fitting and comparing nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

Considering the singly imputed synthetic data generated via plug-in sampling under the multivariate normal model, draws inference procedures including the generalized variance, the sphericity test, the test for independence between two subsets of variables, and the test for the regression of one set of variables on the other. For more details see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>.

Create regular pivot tables with just a few lines of R. More complex pivot tables can also be created, e.g. pivot tables with irregular layouts, multiple calculations and/or derived calculations based on multiple data frames. Pivot tables are constructed using R only and can be written to a range of output formats (plain text, HTML', Latex and Excel'), including with styling/formatting.

This package provides ggplot2 extensions to construct glyph-maps for visualizing seasonality in spatiotemporal data. See the Journal of Statistical Software reference: Zhang, H. S., Cook, D., Laa, U., Langrené, N., & Menéndez, P. (2024) <doi:10.18637/jss.v110.i07>. The manuscript for this package is currently under preparation and can be found on GitHub at <https://github.com/maliny12/paper-sugarglider>.

This package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.

Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

This package parses a fitted R model object, and returns a formula in Tidy Eval code that calculates the predictions. It works with several database backends because it leverages dplyr and dbplyr for the final SQL translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.

MsgPack header files are provided for use by R packages, along with the ability to access, create and alter MsgPack objects directly from R. MsgPack is an efficient binary serialization format. It lets you exchange data among multiple languages like JSON but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the msgpack-c implementation for C and C++(11) for use by R, particularly Rcpp'. The included msgpack-c headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files COPYRIGHTS and AUTHORS for a full list of copyright holders and contributors to msgpack-c'.

Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the Hit Rate is calculated), RR@K (reciprocal rank at k, from which the MRR or mean reciprocal rank is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.

This package provides methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).