Random Cluster Generation (with Specified Degree of Separation)

We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.

Convex Clustering Methods and Clustering Indexes

Convex Clustering methods, including K-means algorithm, On-line Update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning), and calculation of several indexes for finding the number of clusters in a data set.

Statistical Procedures for Agricultural Research

Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.

Hexagonal Hierarchical Geospatial Indexing System

Provides access to Uber's 'H3' geospatial indexing system via 'h3lib' < https://CRAN.R-project.org/package=h3lib>. 'h3r' is designed to mimic the 'H3' Application Programming Interface (API) < https://h3geo.org/docs/api/indexing/>, so that any function in the API is also available in 'h3r'.

Fast and Simple 'MongoDB' Client for R

High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: < https://jeroen.github.io/mongolite/>.

Environments Behaving (Almost) as Lists

List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.

Submission Confidence Index Engine

Converts standardized R4SUB (R for Regulatory Submission) evidence into indicator scores, pillar scores, and a Submission Confidence Index (SCI). Provides sensitivity analysis, explainability tables, and decision band classification to answer the question: are we ready for regulatory submission.

Access Uber's H3 Library

Provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile 'h3-js' < https://github.com/uber/h3-js> and 'V8' < https://github.com/jeroen/v8>.

Classes and Methods for Fast Memory-Efficient Boolean Selections

Provided are classes for boolean and skewed boolean vectors, fast boolean methods, fast unique and non-unique integer sorting, fast set operations on sorted and unsorted sets of integers, and foundations for ff (range index, compression, chunked processing).

Estimation of the Extremal Index

Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) and Berghaus and Bucher (2018) . Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) ). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) , the K-gaps model of Suveges and Davison (2010) and a similar approach of Holesovsky and Fusek (2020) that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.