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Critical Line Algorithm in Pure R
Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical
mean-variance portfolio optimization, see Markowitz (1952)
Variable Length Markov Chains ('VLMC') Models
Functions, Classes & Methods for estimation, prediction, and simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
Construct Graphs of S4 Class Hierarchies
Construct directed graphs of S4 class hierarchies and visualize them. In general, these graphs typically are DAGs (directed acyclic graphs), often simple trees in practice.
Rounding to Decimal Digits
Decimal rounding is non-trivial in binary arithmetic. ISO standard round to even is more rare than typically assumed as most decimal fractions are not exactly representable in binary. Our 'roundX()' versions explore differences between current and potential future versions of round() in R. Further, provides (some partly related) C99 math lib functions not in base R.
Simple Component Analysis
Simple Component Analysis (SCA) often provides much more interpretable components than Principal Components (PCA) while still representing much of the variability in the data.
Supervised Clustering of Predictor Variables Such as Genes
Methodology for supervised grouping aka "clustering" of potentially many predictor variables, such as genes etc, implementing algorithms 'PELORA' and 'WILMA'.
Plug-in Kernel Density Estimation
Kernel density estimation with global bandwidth selection via "plug-in".
Generalized Linear Mixed Models using Template Model Builder
Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
Fitting Single and Mixture of Generalised Lambda Distributions
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments"
Bayesian Linear Mixed-Effects Models
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013)