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Print Maps, Draw on Them, Scan Them Back in
Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects.
Data Validation Infrastructure
Declare data validation rules and data quality indicators;
confront data with them and analyze or visualize the results.
The package supports rules that are per-field, in-record,
cross-record or cross-dataset. Rules can be automatically
analyzed for rule type and connectivity. Supports checks implied
by an SDMX DSD file as well. See also Van der Loo
and De Jonge (2018)
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
Super Learner Prediction
Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Distances on Directed Graphs
Distances on dual-weighted directed graphs using
priority-queue shortest paths (Padgham (2019)
Dynamic Function-Oriented 'Make'-Like Declarative Pipelines
Pipeline tools coordinate the pieces of computationally
demanding analysis projects.
The 'targets' package is a 'Make'-like pipeline tool for statistics and
data science in R. The package skips costly runtime for tasks
that are already up to date,
orchestrates the necessary computation with implicit parallel computing,
and abstracts files as R objects. If all the current output matches
the current upstream code and data, then the whole pipeline is up
to date, and the results are more trustworthy than otherwise.
The methodology in this package
borrows from GNU 'Make' (2015, ISBN:978-9881443519)
and 'drake' (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.
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)