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 implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

This started out as a package for file and string manipulation. Since then, the fs and strex packages emerged, offering functionality previously given by this package. Those packages have hence almost pushed filesstrings into extinction. However, it still has a small number of unique, handy file manipulation functions which can be seen in the vignette. One example is a function to remove spaces from all file names in a directory.

This package provides a collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) and Wang and Fang (2005). The design types included are Audze-Eglais, MaxiMin, and uniform.

This package provides a color picker that can be used as an input in Shiny apps or Rmarkdown documents. The color picker supports alpha opacity, custom color palettes, and many more options. A plot color helper tool is available as an RStudio Addin, which helps you pick colors to use in your plots. A more generic color picker RStudio Addin is also provided to let you select colors to use in your R code.

This package implements functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the spatstat family of packages. Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

The package converts the input in any one of character, integer, numeric, factor, or an ordered type into POSIXct (or Date) objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing.

This package contains a function to do exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS).

This package provides the full texts for Jane Austen's six completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion".

Designed for simplicity, a mirai evaluates an R expression asynchronously in a parallel process, locally or distributed over the network. The result is automatically available upon completion. Modern networking and concurrency, built on nanonext and NNG (Nanomsg Next Gen), ensures reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS. Distributed computing can launch remote resources via SSH or cluster managers. An inherently queued architecture handles many more tasks than available processes, and requires no storage on the file system. Innovative features include support for otherwise non-exportable reference objects, event-driven promises, and asynchronous parallel map.

This package provides a %dopar% adapter such that any type of futures can be used as backends for the foreach framework.

This package offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. The bias-correction is an estimate of the bias term added to regularized discriminant analysis that minimizes the overall risk.

This package provides a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation approach to adaptive smoothing, the Intersecting Confidence Intervals (ICI), variational approaches, and a non-local means filter.

Recipes is an extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.

This package implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. Available measures include adjusted Rand index (ARI), normalized information distance (NID), normalized mutual information (NMI), adjusted mutual information (AMI), normalized variation information (NVI) and entropy.

This package generates ROC plots. Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.

This package implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and Matrix-like objects.

This package estimates previously compiled regression models using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

This package enables you to estimate the p-values for predictors x against target variable y in Lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic.

This package provides two high quality and fast PPRNGs that may be used in an OpenMP parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence.

This package provides classes and methods for handling genetic data. It includes classes to represent genotypes and haplotypes at single markers up to multiple markers on multiple chromosomes. Function include allele frequencies, flagging homo/heterozygotes, flagging carriers of certain alleles, estimating and testing for Hardy-Weinberg disequilibrium, estimating and testing for linkage disequilibrium, ...

This package provides various tools for developers of R packages interfacing with Stan, including functions to set up the required package structure, S3 generics and default methods to unify function naming across Stan-based R packages, and vignettes with recommendations for developers.

This package lets you take formulas including random-effects components (formatted as in lme4, glmmTMB, etc.) and process them. It includes various helper functions.

This package provides methods for spatial data analysis, especially raster data. The included methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported.