Density Deconvolution Using Bayesian Semiparametric Methods
Density deconvolution using bayesian semiparametric methods
Visualization of bdeconv_result object using ggplot2
Density Deconvolution Using Bayesian Semiparametric Methods
Obtaining MCMC samples from deconvoluted density
Constructor for mv_res Objects
Constructor for nc_res Objects
Constructor for zi_res Objects
Generate a B-spline Basis Matrix
Generate a B-spline Cumulative Basis Matrix
Normalize B-Spline Parameters
The Quadratic B-Spline Distribution
Error Density Evaluation
Estimated density function for the true variable(s)
Error Distribution Evaluation
Estimated cumulative distribution function for the true variable(s)
Maximization function for B-spline distribution parameter initializati...
Maximization function for B-spline zero-inflation probability function...
Maximization function for variance function B-spline parameter initial...
Computing mean of MCMC samples
Generate a Penalty Matrix
Generate a Penalty Matrix
Visualization of bdeconv_result object
Printing summary of MCMC samples for bdeconv_result object
Generate a Proposal Variance Matrix for B-Spline Parameters
Generate a Proposal Variance Matrix for B-Spline Parameters for episod...
Register autoplot methods to ggplot2
Register S3 Methods from External Packages
The Restricted Two-Component Normal Distribution
Subset Methods for bdeconv_result object
The Truncated Normal Distribution
Variance Scaling For Mixtures of Restricted Two-Component Normals
Error Variance as a function of X
Estimates the density of a variable in a measurement error setup, potentially with an excess of zero values. For more details see Sarkar (2022) <doi:10.1080/01621459.2020.1782220>.