This package contains a collection of various functions to assist in R programming, such as tools to assist in developing, updating, and maintaining R and R packages, calculating the logit and inverse logit transformations, tests for whether a value is missing, empty or contains only NA and NULL values, and many more.

The gg.gap function enables you to define segments for the y-axis in a ggplot2 plot.

Subject recruitment for medical research is challenging. Slow patient accrual leads to delay in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. This package provides an implementation of a Bayesian method that integrates researcher's experience on previous trials and data from the current study, providing reliable prediction on accrual rate for clinical studies. It provides functions for Bayesian accrual prediction which can be easily used by statisticians and clinical researchers.

The tictoc package provides the timing functions tic and toc that can be nested. It provides an alternative to system.time() with a different syntax similar to that in another well-known software package. tic and toc are easy to use, and are especially useful when timing several sections in more than a few lines of code.

Generate a colorized diff of two R objects for an intuitive visualization of their differences.

Multivariate data analysis is the simultaneous observation of more than one characteristic. In contrast to the analysis of univariate data, in this approach not only a single variable or the relation between two variables can be investigated, but the relations between many attributes can be considered. For the statistical analysis of chemical data one has to take into account the special structure of this type of data. This package contains about 30 functions, mostly for regression, classification and model evaluation and includes some data sets used in the R help examples. It was designed as a R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

This package provides .C64(), an enhanced version of .C() and .Fortran() from the R foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.

This package provides multiple pairwise tests.

Compare complex R objects and reveal the key differences. This package was designed particularly for use in testing packages where being able to quickly isolate key differences makes understanding test failures much easier.

This package was previously an R wrapper of the ARPACK library, and now a shell of the R package RSpectra, an R interface to the Spectra library for solving large scale eigenvalue/vector problems. The current version of rARPACK simply imports and exports the functions provided by RSpectra. New users of rARPACK are advised to switch to the RSpectra package.

This package implements targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality.

Create tree structures from hierarchical data, and traverse the tree in various orders. Aggregate, cumulate, print, plot, convert to and from data.frame and more. This is useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications.

This package provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution with parameterization based on Vose (2008). The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.

This package provides a collection of tools to make working with physical measurements easier. One can convert between metric and imperial units, or calculate a dimension's unknown value from other dimensions' measurements.

Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020), arXiv:2009.06182.

This package combines a forecast of a time series, using the function forecast, with the dynamic plots from dygraphs.

This package performs penalized multivariate analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis.

This package provides a set of predicates and assertions for checking the state and capabilities of R, the operating system it is running on, and the IDE being used. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package provides Ace editor bindings to enable a rich text editing environment within Shiny.

This package provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as the bifurcation structure of any data type.

This package provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.

Learn vector representations of sentences, paragraphs or documents by using the Paragraph Vector algorithms, namely the distributed bag of words (PV-DBOW) and the distributed memory (PV-DM) model. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the doc2vec algorithm. Next it maps these document embeddings to a lower-dimensional space using the Uniform Manifold Approximation and Projection (UMAP) clustering algorithm and finds dense areas in that space using a Hierarchical Density-Based Clustering technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic.

This package provides an enum-type representation of vectors and representation of intervals, including a method of coercing variables in data frames.

This package lets you assign, extract, or remove variable labels from R vectors.