Found 83 packages in 0.03 seconds
Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Parallel Processing Options for Package 'dataRetrieval'
Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022
Ensemble Clustering using K Means and Hierarchical Clustering
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Fitting (Exponential/Diffusion) RT-MPT Models
Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models
by Klauer and Kellen (2018)
Automated and Early Detection of Disease Outbreaks
A powerful tool for automating the early detection of disease outbreaks in time series data. 'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.
Dynamic Models for Confidence and Response Time Distributions
Provides density functions for the joint distribution of
choice, response time and confidence for discrete confidence judgments
as well as functions for parameter fitting, prediction and simulation
for various dynamical models of decision confidence. All models are
explained in detail by Hellmann et al. (2023;
Preprint available at < https://osf.io/9jfqr/>, published version:
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
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 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)
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)