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Multivariate ARIMA and ARIMA-X Analysis
Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima()) and simulation (marima.sim()).
Track Estimation using YAPS (Yet Another Positioning Solver)
Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017)
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.
Convert Gene IDs Between Each Other and Fetch Annotations from Biomart
Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the 'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes" can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol() attempts to determine the most likely current Gene Symbol.
Automated Transcriptome Classifier Pipeline: Comprehensive Transcriptome Analysis
An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the 'pamr' < http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.
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)
Continuous Time Stochastic Modelling using Template Model Builder
Perform state and parameter inference, and forecasting, in
stochastic state-space systems using the 'ctsmTMB' class. This class,
built with the 'R6' package, provides a user-friendly interface for
defining and handling state-space models. Inference is based on
maximum likelihood estimation, with derivatives efficiently computed
through automatic differentiation enabled by the 'TMB'/'RTMB' packages
(Kristensen et al., 2016)
Estimate Global Clustering in Infectious Disease
Implements various novel and standard clustering statistics and other analyses useful for understanding the spread of infectious disease.
Ensemble Clustering using K Means and Hierarchical Clustering
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Parallel Processing Options for Package 'dataRetrieval'
Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022