Software packages developed by myself and my collaborators:
ADPclust
ADPclust is an R software package for fast clustering data using adaptive density peak detection. It clusters data by finding density peaks in a density-distance plot generated from multivariate density estimation.
Version: 0.7; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package includes an automatic centroids selection and parameter optimization algorithm, which finds the number of clusters and cluster centroids by comparing average silhouettes on a grid of testing clustering results; It also includes an user interactive algorithm that allows the user to manually selects cluster centroids from a two dimensional "density-distance plot".
decon
decon is an R software package to deal with nonparametric measurement error problems using deconvolution kernel methods.
Version: 1.2-2; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.
NPsimex
NPsimex is an R software package to perform nonparametric estimation for contaminated data using Simulation-Extrapolation.
Version: 0.2-1; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package contains a collection of functions to perform nonparametric deconvolution using simulation extrapolation (SIMEX). We propose an estimator that adopts the SIMEX idea but bypasses the simulation step in the original SIMEX algorithm. There is no bandwidth parameter and the estimate is determined by appropriately selecting "design points". See details in: Wang, X.F., Sun, J. and Fan, Z. (2011). Deconvolution density estimation with heteroscedastic errors using SIMEX.
fANOVA
fANCOVA is an R software package to perform nonparametric analysis of covariance for regression curves /surfaces or functional data.
Version: 0.5-1; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package contains a collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators. Reference: Wang, X.F. and Ye, D. (2010). On nonparametric comparison of images and regression surfaces. Journal of Statistical Planning and Inference. 140(10), 2875-2884.
Normalizing bead-based microRNA expression data
A measurement error model-based approach for normalizing bead-based microRNA expression data: The software package developed by my collaborator, Prof. Bin Wang. Reference: Wang, B., Wang, X.F. and Xi, Y. (2011) Normalizing bead-based microRNA expression data: a measurement error model-based approach, Bioinformatics, 27(11), 1506-1512.
MicroRNA microarray data normalization
MicroRNA microarray data normalization using a quantitative real-time PCR based logistic regression model: The software package developed by my collaborator, Prof. Bin Wang. Reference: Wang, B., Wang, X.F., Howell, P., Qian, X., Huang, K., Riker, A. I., Ju, J., and Xi, Y. (2010). A personalized microRNA microarray normalization method using a logistic regression model. Bioinformatics, 26(2), 228-234.
ADPclust
ADPclust is an R software package for fast clustering data using adaptive density peak detection. It clusters data by finding density peaks in a density-distance plot generated from multivariate density estimation.
Version: 0.7; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package includes an automatic centroids selection and parameter optimization algorithm, which finds the number of clusters and cluster centroids by comparing average silhouettes on a grid of testing clustering results; It also includes an user interactive algorithm that allows the user to manually selects cluster centroids from a two dimensional "density-distance plot".
decon
decon is an R software package to deal with nonparametric measurement error problems using deconvolution kernel methods.
Version: 1.2-2; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.
NPsimex
NPsimex is an R software package to perform nonparametric estimation for contaminated data using Simulation-Extrapolation.
Version: 0.2-1; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package contains a collection of functions to perform nonparametric deconvolution using simulation extrapolation (SIMEX). We propose an estimator that adopts the SIMEX idea but bypasses the simulation step in the original SIMEX algorithm. There is no bandwidth parameter and the estimate is determined by appropriately selecting "design points". See details in: Wang, X.F., Sun, J. and Fan, Z. (2011). Deconvolution density estimation with heteroscedastic errors using SIMEX.
fANOVA
fANCOVA is an R software package to perform nonparametric analysis of covariance for regression curves /surfaces or functional data.
Version: 0.5-1; supported platforms: Linux / Windows / MacOS X; download from R - CRAN
This package contains a collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators. Reference: Wang, X.F. and Ye, D. (2010). On nonparametric comparison of images and regression surfaces. Journal of Statistical Planning and Inference. 140(10), 2875-2884.
Normalizing bead-based microRNA expression data
A measurement error model-based approach for normalizing bead-based microRNA expression data: The software package developed by my collaborator, Prof. Bin Wang. Reference: Wang, B., Wang, X.F. and Xi, Y. (2011) Normalizing bead-based microRNA expression data: a measurement error model-based approach, Bioinformatics, 27(11), 1506-1512.
MicroRNA microarray data normalization
MicroRNA microarray data normalization using a quantitative real-time PCR based logistic regression model: The software package developed by my collaborator, Prof. Bin Wang. Reference: Wang, B., Wang, X.F., Howell, P., Qian, X., Huang, K., Riker, A. I., Ju, J., and Xi, Y. (2010). A personalized microRNA microarray normalization method using a logistic regression model. Bioinformatics, 26(2), 228-234.