Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) and Komárek and Komárková (2014, J. of Stat. Soft.) . It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) , Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) , Jaspers, Komárek, Aerts (2018, Biom. J.) and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) .

Small Helpers and Tricks for Epidemics Analysis

A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.

Monazite Dating for the NiLeDAM Team

Th-U-Pb electron microprobe age dating of monazite, as originally described in .

Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples

Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), ). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), ) and other metrics are included.

Time Series Artificial Neural Network

The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. The optimum size of the hidden layers was also determined after determining the number of lags to be included. This package has been developed using the algorithm of Paul and Garai (2021) .

Analysis of Chronological Patterns from Archaeological Count Data

A toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.

Continuous Mapping of Genetic Diversity

Generate continuous maps of genetic diversity using moving windows with options for rarefaction, interpolation, and masking as described in Bishop et al. (2023) .

Estimation of Transmissibility in the Early Stages of a Disease Outbreak

Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) .

ICS via a Shiny Application

Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) ) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2018) ) using a shiny app.

Dynamic Model of Ammonia Emission from Field-Applied Manure

An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. See Hafner et al. (2018) for information on the model and Hafner et al. (2019) for more on the measurement data used for parameter development.