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Comparison of Variance - Covariance Patterns
Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019)
Post-Processing of Markov Chain Monte Carlo Simulations for Chronological Modelling
Statistical analysis of archaeological dates and groups of
dates. This package allows to post-process Markov Chain Monte Carlo
(MCMC) simulations from 'ChronoModel' < https://chronomodel.com/>,
'Oxcal' < https://c14.arch.ox.ac.uk/oxcal.html> or 'BCal'
< https://bcal.shef.ac.uk/>. It provides functions for the study of
rhythms of the long term from the posterior distribution of a series
of dates (tempo and activity plot). It also allows the estimation and
visualization of time ranges from the posterior distribution of groups
of dates (e.g. duration, transition and hiatus between successive
phases) as described in Philippe and Vibet (2020)
'REPPlab' via a Shiny Application
Performs exploratory projection pursuit via 'REPPlab' (Daniel Fischer, Alain Berro, Klaus Nordhausen & Anne Ruiz-Gazen (2019)
Time Series Forecasting using ARIMA-ANN Hybrid Model
Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003)
Generalized Spline Mixed Effect Models for Longitudinal Breath Data
Automated analysis and modeling of longitudinal 'omics' data (e.g. breath 'metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.
Post-Processing of the Markov Chain Simulated by ChronoModel or Oxcal
Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see < http://www.chronomodel.fr> for more information). ChronoModel is a friendly software to construct a chronological model in a Bayesian framework. Its output is a sampled Markov chain from the posterior distribution of dates component the chronology. The functions can also be applied to the analyse of mcmc output generated by Oxcal software.
Stratified-Petersen Analysis System
The Stratified-Petersen Analysis System (SPAS) is designed
to estimate abundance in two-sample capture-recapture experiments
where the capture and recaptures are stratified. This is a generalization
of the simple Lincoln-Petersen estimator.
Strata may be defined in time or in space or both,
and the s strata in which marking takes place
may differ from the t strata in which recoveries take place.
When s=t, SPAS reduces to the method described by
Darroch (1961)
Data Sets for Statistical Methods in Customer Relationship Management by Kumar and Petersen (2012).
Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York.
Convert 'tinytest' Output to JUnit XML
Unit testing is a solid component of automated CI/CD pipelines. 'tinytest' - a lightweight, zero-dependency alternative to 'testthat' was developed. To be able to integrate 'tinytests' results into common CI/CD systems the test results from tinytest need to be caputred and converted to JUnit XML format. 'tinytest2JUnit' enables this conversion while staying also lightweight and only have 'tinytest' as its dependency.
Outlier Detection Using Invariant Coordinate Selection
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.