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Complete Functional Regulation Analysis
Calculates complete functional regulation analysis and visualize
the results in a single heatmap. The provided example data is for biological
data but the methodology can be used for large data sets to compare quantitative
entities that can be grouped. For example, a store might divide entities into
cloth, food, car products etc and want to see how sales changes in the groups
after some event. The theoretical background for the calculations are provided
in New insights into functional regulation in MS-based drug profiling, Ana Sofia
Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports
Item Selection and Exhaustive Search for Rasch Models
Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used.
A Crew Launcher Plugin for AWS Batch
In computationally demanding analysis projects,
statisticians and data scientists asynchronously
deploy long-running tasks to distributed systems,
ranging from traditional clusters to cloud services.
The 'crew.aws.batch' package extends the 'mirai'-powered
'crew' package with a worker launcher plugin for AWS Batch.
Inspiration also comes from packages 'mirai' by Gao (2023)
< https://github.com/shikokuchuo/mirai>,
'future' by Bengtsson (2021)
Crew Launcher Plugins for Traditional High-Performance Computing Clusters
In computationally demanding analysis projects,
statisticians and data scientists asynchronously
deploy long-running tasks to distributed systems,
ranging from traditional clusters to cloud services.
The 'crew.cluster' package extends the 'mirai'-powered
'crew' package with worker launcher plugins for traditional
high-performance computing systems.
Inspiration also comes from packages 'mirai' by Gao (2023)
< https://github.com/shikokuchuo/mirai>,
'future' by Bengtsson (2021)
A Distributed Worker Launcher Framework
In computationally demanding analysis projects,
statisticians and data scientists asynchronously
deploy long-running tasks to distributed systems,
ranging from traditional clusters to cloud services.
The 'NNG'-powered 'mirai' R package by Gao (2023)
Genetic Population Level Functions
This collection of gene representation-independent functions
implements the population layer of extended evolutionary and genetic
algorithms and its support. The population layer consists of functions
for initializing, logging, observing, evaluating a population of genes,
as well as of computing the next population. For parallel evaluation of a
population of genes 4 execution models - named Sequential, MultiCore,
FutureApply, and Cluster - are provided. They are implemented by
configuring the lapply() function. The execution model FutureApply can be
externally configured as recommended by Bengtsson (2021)
Understand and Describe Bayesian Models and Posterior Distributions
Provides utilities to describe posterior
distributions and Bayesian models. It includes point-estimates such as
Maximum A Posteriori (MAP), measures of dispersion (Highest Density
Interval - HDI; Kruschke, 2015
R Fortunes
A collection of fortunes from the R community.