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Pain Assessment at Withdrawal Speeds (PAWS)
Automated pain scoring from paw withdrawal tracking data. Based on
Jones et al. (2020) "A machine-vision approach for automated pain
measurement at millisecond timescales"
Gillespie's Stochastic Simulation Algorithm for Impatient People
A fast, scalable, and versatile framework for
simulating large systems with Gillespie's Stochastic Simulation
Algorithm ('SSA'). This package is the spiritual successor to the
'GillespieSSA' package originally written by Mario Pineda-Krch.
Benefits of this package include major speed improvements (>100x),
easier to understand documentation, and many unit tests that try to
ensure the package works as intended. Cannoodt and Saelens et al. (2021)
Generate Fake Datasets for Prototyping and Teaching
Create fake datasets that can be used for prototyping and teaching. This package provides a set of functions to generate fake data for a variety of data types, such as dates, addresses, and names. It can be used for prototyping (notably in 'shiny') or as a tool to teach data manipulation and data visualization.
Lorem-Ipsum-Like Helpers for Fast Shiny Prototyping
Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers.
An R Interface to the JBrowse 2 Genome Browser
Provides an R interface to the JBrowse 2 genome browser. Enables embedding a JB2 genome browser in a Shiny app or R Markdown document. The browser can also be launched from an interactive R console. The browser can be loaded with a variety of common genomics data types, and can be used with a custom theme.
Access Pinterest API
Get information (boards, pins and users) from the Pinterest < http://www.pinterest.com> API.
Data Structures, Summaries, and Visualisations for Missing Data
Missing values are ubiquitous in data and need to be explored and
handled in the initial stages of analysis. 'naniar' provides data
structures and functions that facilitate the plotting of missing values and
examination of imputations. This allows missing data dependencies to be
explored with minimal deviation from the common work patterns of 'ggplot2'
and tidy data. The work is fully discussed at Tierney & Cook (2023)
Send Email Messages
A light, simple tool for sending emails with minimal dependencies.
An API Client for the Environmental Data Initiative Repository
A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository < https://portal.edirepository.org/nis/home.jsp> is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack < https://pastaplus-core.readthedocs.io/en/latest/index.html#> and was developed in collaboration with the US 'LTER' Network < https://lternet.edu/>. 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users.
Beyond the Border - Kernel Density Estimation for Urban Geography
The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function.
The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid.
The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid.
The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid.
The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.
Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002)