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Exploratory Chemometrics for 2D Spectroscopy
A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly.
Spectroscopy Related Utilities
Utility functions for spectroscopy. 1. Functions to simulate spectra for use in teaching or testing. 2. Functions to process files created by 'LoggerPro' and 'SpectraSuite' software.
Predictive Data Analysis System
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
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.
An Interface to Google's 'BigQuery' 'API'
Easily talk to Google's 'BigQuery' database from R.
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.
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)
Causal Inference using Bayesian Additive Regression Trees
Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012)
Twitter Conversation Networks and Analysis
Collects tweets and metadata for threaded conversations and generates networks.