Found 88 packages in 0.06 seconds
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.
Configural Frequencies Analysis Using Log-Linear Modeling
Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
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
Robust Bayesian T-Test
An implementation of Bayesian model-averaged t-tests that allows
users to draw inferences about the presence versus absence of an effect,
variance heterogeneity, and potential outliers. The 'RoBTT' package estimates
ensembles of models created by combining competing hypotheses and applies
Bayesian model averaging using posterior model probabilities. Users can
obtain model-averaged posterior distributions and inclusion Bayes factors,
accounting for uncertainty in the data-generating process
(Maier et al., 2024,
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.
Fitting (Exponential/Diffusion) RT-MPT Models
Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models
by Klauer and Kellen (2018)
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