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Modify Data Using Externally Defined Modification Rules
Data cleaning scripts typically contain a lot of 'if this change that' type of statements. Such statements are typically condensed expert knowledge. With this package, such 'data modifying rules' are taken out of the code and become in stead parameters to the work flow. This allows one to maintain, document, and reason about data modification rules as separate entities.
Smoothing tools
Tools rewritten in C for various smoothing tasks
Routines for Common fMRI Processing Tasks
Supports fMRI (functional magnetic resonance imaging)
analysis tasks including reading in 'CIFTI', 'GIFTI' and
'NIFTI' data, temporal filtering, nuisance regression, and
aCompCor (anatomical Components Correction) (Muschelli et al.
(2014)
Distances on Directed Graphs
Distances on dual-weighted directed graphs using
priority-queue shortest paths (Padgham (2019)
Radiation Safety
Provides functions for radiation safety, also known as
"radiation protection" and "radiological control". The science of
radiation protection is called "health physics" and its engineering
functions are called "radiological engineering". Functions in this
package cover many of the computations needed by radiation safety
professionals. Examples include: obtaining updated calibration and
source check values for radiation monitors to account for radioactive
decay in a reference source, simulating instrument readings to better
understand measurement uncertainty, correcting instrument readings
for geometry and ambient atmospheric conditions. Many of these
functions are described in Johnson and Kirby (2011, ISBN-13:
978-1609134198). Utilities are also included for developing inputs
and processing outputs with radiation transport codes, such as MCNP,
a general-purpose Monte Carlo N-Particle code that can be used for
neutron, photon, electron, or coupled neutron/photon/electron transport
(Werner et. al. (2018)
Identify Event Sequences Using Time Series Joins
Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.
Advanced Policing Techniques for the Board Game "Letters from Whitechapel"
Provides a set of functions to make tracking the hidden movements of the 'Jack' player easier. By tracking every possible path Jack might have traveled from the point of the initial murder including special movement such as through alleyways and via carriages, the police can more accurately narrow the field of their search. Additionally, by tracking all possible hideouts from round to round, rounds 3 and 4 should have a vastly reduced field of search.
Generates Expectations for 'testthat' Unit Testing
Helps systematize and ease the process of building unit tests with the 'testthat' package by providing tools for generating expectations.
Managing Larger Data on a GitHub Repository
Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. This package provides a simple work-around by allowing larger (up to 2 GB) data files to piggyback on a repository as assets attached to individual GitHub releases. These files are not handled by git in any way, but instead are uploaded, downloaded, or edited directly by calls through the GitHub API. These data files can be versioned manually by creating different releases. This approach works equally well with public or private repositories. Data can be uploaded and downloaded programmatically from scripts. No authentication is required to download data from public repositories.
Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs
Tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and
to analyze data from these designs, using aggregate and individual methods such as: difference
scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013)