Structural Bayesian Information Criterion for Graphical Models

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.

R Interface to 'EPP-Lab', a Java Program for Exploratory Projection Pursuit

An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.

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) .

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.

Spatial Ecology Miscellaneous Methods

Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) for details.

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) .

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) .

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.