Simple Git Client for R

Simple git client for R based on 'libgit2' < https://libgit2.org> with support for SSH and HTTPS remotes. All functions in 'gert' use basic R data types (such as vectors and data-frames) for their arguments and return values. User credentials are shared with command line 'git' through the git-credential store and ssh keys stored on disk or ssh-agent.

An Implementation of 'Salesforce' APIs Using Tidy Principles

Functions connecting to the 'Salesforce' Platform APIs (REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) < https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. "API" is an acronym for "application programming interface". Most all calls from these APIs are supported as they use CSV, XML or JSON data that can be parsed into R data structures. For more details please see the 'Salesforce' API documentation and this package's website < https://stevenmmortimer.github.io/salesforcer/> for more information, documentation, and examples.

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.

Missing Data Imputation and Model Checking

The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

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) . The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.

Twitter Conversation Networks and Analysis

Collects tweets and metadata for threaded conversations and generates networks.

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) .