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Compute Neural Fragility for Ictal iEEG Time Series
Provides tools to compute the neural fragility matrix from intracranial electrocorticographic (iEEG) recordings, enabling the analysis of brain dynamics during seizures. The package implements the method described by Li et al. (2017)
Determining the Best Number of Clusters in a Data Set
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Process Based Epidemiological Model for Cercospora Leaf Spot of Sugar Beet
Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.
Flexible Procedures for Clustering
Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.
Generalized Price and Quantity Indexes
Tools to build and work with bilateral generalized-mean
price indexes (and by extension quantity indexes), and indexes composed of
generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS).
Covers the core mathematical machinery for making bilateral price indexes,
computing price relatives, detecting outliers, and decomposing indexes,
with wrappers for all common (and many uncommon) index-number
formulas. Implements and extends many of the methods in
Balk (2008,
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.
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.
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/>.
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'.
Validation of Clustering Results
Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008)