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BDWreg - Bayesian Inference for Discrete Weibull Regression

A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.

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2.00 score 4 scripts 213 downloads

SmoothWin - Soft Windowing for Linear and Non-Linear Models

Fits symmetric soft windowing to linear and non-linear models by assigning exponential weights over time around specified modes; bandwidth and sharpness of the windows are chosen by a grid search and comparison diagnostics (Hamed Haselimashhadi et al (2019) <doi:10.1093/bioinformatics/btz744>).

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1.00 score 5 scripts 18 downloads

SmoothWin - Soft Windowing for Linear and Non-Linear Models

Fits symmetric soft windowing to linear and non-linear models by assigning exponential weights over time around specified modes; bandwidth and sharpness of the windows are chosen by a grid search and comparison diagnostics (Hamed Haselimashhadi et al (2019) <doi:10.1093/bioinformatics/btz744>).

Last updated

1.00 score 5 scripts 18 downloads

DREGAR - Regularized Estimation of Dynamic Linear Regression in the Presence of Autocorrelated Residuals (DREGAR)

A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.

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1.00 score 1 stars 5 scripts 678 downloads

DLASSO - Implementation of Adaptive or Non-Adaptive Differentiable Lasso and SCAD Penalties in Linear Models

An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.

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1.00 score 1 stars 3 scripts 147 downloads