Analysis of terms in linear, generalized and mixed linear models, 
	on the basis of multiple comparisons of factor contrasts.  Specially suited 
	for the analysis of interaction terms.

Analysis of features by phi delta diagrams. In particular, functions for reading data and calculating phi and delta as well as the functionality to plot it. Moreover it is possible to do further analysis on the data by generating rankings. For more information on phi delta diagrams, see also Giuliano Armano (2015) <doi:10.1016/j.ins.2015.07.028>.

Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Control Philips Hue smart lighting. Use this package to
    connect to a Hue bridge on your local network (remote authentication
    not yet supported) and control your smart lights through the Philips
    Hue API. All API V1 endpoints are supported. See API documentation at
    <https://developers.meethue.com/>.

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.

A bioinformatics method developed for analyzing the heterogeneity of single-cell populations. Phitest provides an objective and automatic method to evaluate the performance of clustering and quality of cell clusters.

Functions to extract and handle commonly occurring principal phrases
    obtained from collections of texts. Major speed improvements - core functions 
    rewritten in C++ for faster phrase-document parsing, clustering, and text 
    distance computations. Based on, Small, E., & Cabrera, J. (2025). Principal 
    phrase mining, an automated method for extracting meaningful phrases from 
    text. International Journal of Computers and Applications, 47(1), 84–92.