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Computational Geometry
R interface to (some of) cddlib (< https://github.com/cddlib/cddlib>). Converts back and forth between two representations of a convex polytope: as solution of a set of linear equalities and inequalities and as convex hull of set of points and rays. Also does linear programming and redundant generator elimination (for example, convex hull in n dimensions). All functions can use exact infinite-precision rational arithmetic.
Indices of Effect Size
Provide utilities to work with indices of effect size for a wide
variety of models and hypothesis tests (see list of supported models using
the function 'insight::supported_models()'), allowing computation of and
conversion between indices such as Cohen's d, r, odds, etc.
References: Ben-Shachar et al. (2020)
A Collection of Efficient and Extremely Fast R Functions
A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1
Extending 'gt' for Beautiful HTML Tables
Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions.
Time Series Modeling for Air Pollution and Health
Tools for specifying time series regression models.
Social Relation Model (SRM) Analyses for Single or Multiple Groups
Social Relation Model (SRM) analyses for single or multiple
round-robin groups are performed. These analyses are either based on one
manifest variable, one latent construct measured by two manifest variables,
two manifest variables and their bivariate relations, or two latent
constructs each measured by two manifest variables. Within-group t-tests
for variance components and covariances are provided for single groups.
For multiple groups two types of significance tests are provided:
between-groups t-tests (as in SOREMO) and enhanced standard errors based on
Lashley and Bond (1997)
Normal aka Gaussian 1-d Mixture Models
Onedimensional Normal (i.e. Gaussian) Mixture Models (S3) Classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities. Efficient random number generation and graphics. Fitting to data by efficient ML (Maximum Likelihood) or traditional EM estimation.
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.
SPArse Matrix
Set of functions for sparse matrix algebra.
Differences with other sparse matrix packages are:
(1) we only support (essentially) one sparse matrix format,
(2) based on transparent and simple structure(s),
(3) tailored for MCMC calculations within G(M)RF.
(4) and it is fast and scalable (with the extension package spam64).
Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010)
Support Functions and Data for "Ecological Models and Data"
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).