How to Add Two R Tables

Methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses 'disordR' discipline (Hankin, 2022, ). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) .

Non-Ordered Vectors

Functionality for manipulating values of associative maps. The package is a dependency for mvp-type packages that use the STL map class: it traps plausible idiom that is ill-defined (implementation-specific) and returns an informative error, rather than returning a possibly incorrect result. To cite the package in publications please use Hankin (2022) .

Stochastic Robust Multivariate Statistics

Algorithms for robust multivariate statistics (STochAstic Robust multivaRiate Statistics) including geometric median and geometric median covariance computation, k-medians clustering and robust median Principal Compenents Analysis (PCA), robust estimation of parameters for Gaussian, Student, or Laplace mixture models. 'STARRS' provides an independent, clean, consolidated and cohesive framework, while drawing inspiration from the approaches implemented in packages 'Gmedian', 'Kmedians', 'RGMM', 'RobRegression'. Methods used in the package refer to H. Robbins, S. Monro (1951) ; D. Kraus, V. M. Panaretos (2012) ; H. Cardot, A. Godichon-Baggioni (2015) ; A. Godichon-Baggioni, S. Robin (2024) .

Display Tournament Fixtures using Knock Out and Round Robin Techniques

Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by 'Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).

Example Data Sets for Causal Inference Textbooks

Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" < https://theeffectbook.net>, first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" < https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.

A SPARQL Client for R

Provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format.

Joint Segmentation of Correlated Time Series

It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) .

The Davies Quantile Function

Various utilities for the Davies distribution.

Tools for the 'Parallel' Package

Miscellaneous utilities for parallelizing large computations. Alternative to MapReduce. File splitting and distributed operations such as sort and aggregate. "Software Alchemy" method for parallelizing most statistical methods, presented in N. Matloff, Parallel Computation for Data Science, Chapman and Hall, 2015. Includes a debugging aid.

Manipulate and Analyze Round Robin Dyadic Data

Provides utilities for processing and analyzing dyadic data collected using a round-robin design, in which each person in a group rates or interacts with every other person on at least one variable. Data manipulation functions prepare datasets for dyadic data analysis by creating the actor and partner dummy variables required by the social relations model (SRM). Analysis functions implement the SRM using multilevel modeling via a custom 'nlme' covariance class ('pdSRM'), following the approach of Snijders and Kenny (1999) and Knight and Humphrey (2019) . The package estimates group, actor, partner, and relationship variance components along with generalized and dyadic reciprocity correlations, and supports both null and fixed-effects models.