SOPC - The Sparse Online Principal Component Estimation Algorithm

The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

Last updated 2 years ago

2.77 score 3 dependents 11 scripts 272 downloads

COR - The COR for Optimal Subset Selection in Distributed Estimation

An algorithm of optimal subset selection, related to Covariance matrices, observation matrices and Response vectors (COR) to select the optimal subsets in distributed estimation. The philosophy of the package is described in Guo G. (2024) <doi:10.1007/s11222-024-10471-z>.

Last updated 2 months ago

2.41 score 13 scripts 572 downloads

ISR - The Iterated Score Regression-Based Estimation

We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Last updated 24 days ago

2.08 score 2 scripts 431 downloads

LIC - The LIC Criterion for Optimal Subset Selection

The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) <doi:10.1080/02664763.2022.2053949>.

Last updated 3 years ago

1.70 score 1 scripts 184 downloads

Dogoftest - Distributed Online Goodness-of-Fit Tests for Distributed Datasets

Distributed Online Goodness-of-Fit Test can process the distributed datasets. The philosophy of the package is described in Guo G.(2024) <doi:10.1016/j.apm.2024.115709>.

Last updated 2 days ago

1.00 score

TFM - Sparse Online Principal Component for TFM

The Truncated Factor Model is a statistical model designed to handle specific data structures in data analysis. This R package focuses on the Sparse Online Principal Component Estimation method, which is used to calculate data such as the loading matrix and specific variance matrix for truncated data, thereby better explaining the relationship between common factors and original variables. Additionally, the R package also provides other equations for comparison with the Sparse Online Principal Component Estimation method.The philosophy of the package is described in Guangbao Guo. (2023) <doi:10.1007/s00180-022-01270-z>.

Last updated 3 months ago

1.00 score 412 downloads

SFM - A Package for Analyzing Skew Factor Models

Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC) method to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix. Additionally, it includes robust regression methods such as adaptive Huber regression.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Last updated 3 months ago

1.00 score 6 scripts 446 downloads

LLIC - Likelihood Criterion (LIC) Analysis for Laplace Regression Model

Performs likelihood criterion analysis using the Laplace regression model to determine its optimal subset of variables. The methodology is based on Guo et al. (2023), LIC criterion for optimal subset selection in distributed interval estimation <doi:10.1080/02331888.2020.1823979>.

Last updated 3 months ago

1.00 score

DIRMR - Distributed Imputation for Random Effects Models with Missing Responses

By adding over-relaxation factor to PXEM (Parameter Expanded Expectation Maximization) method, the MOPXEM (Monotonically Overrelaxed Parameter Expanded Expectation Maximization) method is obtained. Compare it with the existing EM (Expectation-Maximization)-like methods. Then, distribute and process five methods and compare them, achieving good performance in convergence speed and result quality.The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

Last updated 3 months ago

1.00 score 207 downloads

LFM - Laplace Factor Model Analysis and Evaluation

Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Parallel Principal Component (PPC), Sparse Approximate Principal Component (SAPC), Standard Principal Component (SPC), and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.

Last updated 3 months ago

1.00 score 1 scripts

DTSR - Distributed Trimmed Scores Regression for Handling Missing Data

Provides functions for handling missing data using Distributed Trimmed Scores Regression and other imputation methods. It includes facilities for data imputation, evaluation metrics, and clustering analysis. It is designed to work in distributed computing environments to handle large datasets efficiently. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Last updated 3 months ago

1.00 score 548 downloads

FPCdpca - The FPCdpca Criterion on Distributed Principal Component Analysis

We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.

Last updated 9 months ago

1.00 score 160 downloads

ORKM - The Online Regularized K-Means Clustering Algorithm

Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.

Last updated 10 months ago

1.00 score 1 scripts 661 downloads

DLEGFM - Distributed Loading Estimation for General Factor Model

The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

Last updated 12 months ago

1.00 score 176 downloads

DLPCA - The Distributed Local PCA Algorithm

Algorithm to handle with optimal subset selection for distributed local principal component analysis. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.

Last updated 3 years ago

1.00 score 1 scripts 179 downloads