Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Chronological Objects which Can Handle Dates and Times

Provides chronological objects which can handle dates and times.

Various R Programming Tools for Data Manipulation

Various R programming tools for data manipulation, including medical unit conversions, combining objects, character vector operations, factor manipulation, obtaining information about R objects, generating fixed-width format files, extracting components of date & time objects, operations on columns of data frames, matrix operations, operations on vectors, operations on data frames, value of last evaluated expression, and a resample() wrapper for sample() that ensures consistent behavior for both scalar and vector arguments.

Prototype Object-Based Programming

An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas.

Constrained Nonlinear Optimization

Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

Fast and Robust Multi-Scale Graph Clustering

A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) . The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can be coerced into a matrix.

A Replacement and Extension of the 'optim' Function

Provides a test of replacement and extension of the optim() function to unify and streamline optimization capabilities in R for smooth, possibly box constrained functions of several or many parameters. This version has a reduced set of methods and is intended to be on CRAN.

An Image Processing Toolkit

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, . The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

Graphical Displays for Structured Problem Solving and Diagnosis

Powerful graphical displays and statistical tools for structured problem solving and diagnosis. The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique. The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file. References: David Hartshorne (2019, ISBN: 978-1-5272-5139-7). Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467).

Regression Estimation and Presentation

A collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in < https://pj.freefaculty.org/guides/>. Includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview. The vignette 'Rstyle' has advice about coding in R. The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'.