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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.
MARS Based ANN Hybrid Model
Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.
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)
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.
Chronological Bayesian Models Integrating Optically Stimulated Luminescence and Radiocarbon Age Dating
Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017)
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.
Species-Richness Prediction and Diversity Estimation with R
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Experiment-Selector CV-TMLE for Integration of Observational and RCT Data
The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022)