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Read Untidy Excel Files
Imports non-tabular from Excel files into R. Exposes cell content, position and formatting in a tidy structure for further manipulation. Tokenizes Excel formulas. Supports '.xlsx' and '.xlsm' via the embedded 'RapidXML' C++ library < https://rapidxml.sourceforge.net>. Does not support '.xlsb' or '.xls'.
Finding Rhythms Using Extended Circadian Harmonic Oscillators (ECHO)
Provides a function (echo_find()) designed to find rhythms
from data using extended harmonic oscillators. For more information,
see H. De los Santos et al. (2020)
Functions, Data Sets and Vignettes to Aid in Learning Principal Components Analysis (PCA)
Principal component analysis (PCA) is one of the most widely used data analysis techniques. This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.
2D and 3D Hive Plots for R
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.
Interactive Exploration of Contour Data
Interactive tools to explore topographic-like data sets. Such data sets take the form of a matrix in which the rows and columns provide location/frequency information, and the matrix elements contain altitude/response information. Such data is found in cartography, 2D spectroscopy and chemometrics. The functions in this package create interactive web pages showing the contoured data, possibly with slices from the original matrix parallel to each dimension. The interactive behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Robust Expectation-Maximization Estimation for Latent Variable Models
Traditional latent variable models assume that the population
is homogeneous, meaning that all individuals in the population are
assumed to have the same latent structure. However, this assumption is
often violated in practice given that individuals may differ in their
age, gender, socioeconomic status, and other factors that can affect
their latent structure. The robust expectation maximization (REM)
algorithm is a statistical method for estimating the parameters of a
latent variable model in the presence of population heterogeneity as recommended by
Nieser & Cochran (2023)
Twitter Conversation Networks and Analysis
Collects tweets and metadata for threaded conversations and generates networks.
Create Common TLGs Used in Clinical Trials
Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.
Compact and Flexible Summaries of Data
A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.
Collecting Social Media Data and Generating Networks for Analysis
A suite of easy to use functions for collecting social media data and generating networks for analysis. Supports Twitter, YouTube, Reddit and web site data sources.