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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>.

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2.91 score 3 dependents 15 scripts 186 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>.

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2.11 score 13 scripts 442 downloads

Desimml - Distributed Estimation in Single-Index Models with Multiple Links ('Desimml')

Provides a user-friendly wrapper for fitting Distributed Single-Index Models with Multiple Links ('SIMML'). It estimates a linear combination (single-index) of baseline covariates X and models the treatment-specific outcome y via treatment-specific, non-parametrically estimated link functions. This allows flexible modelling of complex relationships between covariates, treatments and outcomes.

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2.00 score 499 downloads

DLFM - Distributed Laplace Factor Model

Distributed estimation method is based on a Laplace 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>.

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1.78 score 193 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>.

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1.78 score 2 scripts 679 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>.

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1.70 score 1 scripts 203 downloads

GulFM - General Unilateral Load Estimator for Two-Layer Latent Factor Models

Implements general unilateral loading estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions. A unified interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) <doi:10.1016/j.apm.2025.116280>.

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1.48 score 109 downloads

CFM - Analyzing Censored Factor Models

Provides generation and estimation of censored factor models for high-dimensional data with censored errors (normal, t, logistic). Includes Sparse Orthogonal Principal Components (SOPC), and evaluation metrics. Based on Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.48 score 140 downloads

EFM - Elliptical Factor Models

The elliptical factor model, as an extension of the traditional factor model, effectively overcomes the limitations of the traditional model when dealing with heavy-tailed characteristic data. This package implements sparse principal component methods (SPC) and bi-sparse online principal component estimation (SPOC) for parameter estimation. Includes functionality for calculating mean squared error, relative error, and loading matrix sparsity.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.30 score 3 scripts 173 downloads

Docovt - Distributed Online Covariance Matrix Tests

Distributed Online Covariance Matrix Tests 'Docovt' is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Docovt' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of 'Docovt' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

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1.30 score 151 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>.

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1.30 score 168 downloads

DLMRMV - Distributed Linear Regression Models with Response Missing Variables

As a distributed imputation strategy, the Distributed full information Multiple Imputation method is developed to impute missing response variables in distributed linear regression. The philosophy of the package is described in 'Guo' (2025) <doi:10.1038/s41598-025-93333-6>.

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1.30 score 185 downloads

OPCreg - Online Principal Component Regression for Online Datasets

The online principal component regression method can process the online data set. 'OPCreg' implements the online principal component regression method, which is specifically designed to process online datasets efficiently. This method is particularly useful for handling large-scale, streaming data where traditional batch processing methods may be computationally infeasible.The philosophy of the package is described in 'Guo' (2025) <doi:10.1016/j.physa.2024.130308>.

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1.30 score 511 downloads

OSIRCR - Cosine Regression-Based Online Sliced Inverse Regression Algorithm

In high-dimensional streaming data analysis, extracting core periodic features under real-time constraints remains challenging. Traditional dimension reduction methods fail to adapt to incremental data and yield low accuracy due to irrelevant variables. This package provides the Online Sliced Inverse Regression framework for cosine regression with high-dimensional irrelevant variables. It integrates subspace extraction of sliced inverse regression and incremental learning of online algorithms to efficiently handle periodic streaming data. Cai, Z., Li, R., & Zhu, L. (2020) <doi:10.48550/arXiv.2002.02795>.

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1.00 score

BFM - Beta Factor Model

Provides tools for factor analysis in financial and econometric settings under Beta factor models. It includes functions to simulate factor-model data with Beta-distributed idiosyncratic components (e.g., standard Beta, scaled Beta, and truncated Beta distributions) and to conduct model diagnostic assessments such as likelihood ratio tests for factor number selection and goodness-of-fit tests for Beta distribution assumptions. Estimation routines encompass maximum likelihood estimation for finite-dimensional Beta factor models, regularized Beta factor analysis for high-dimensional datasets, and shrinkage-based estimation for robust Beta factor loading recovery in noisy or incomplete data environments. The package's methodological framework is detailed in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.00 score

BFM - Beta Factor Model

Provides tools for factor analysis in financial and econometric settings under Beta factor models. It includes functions to simulate factor-model data with Beta-distributed idiosyncratic components (e.g., standard Beta, scaled Beta, and truncated Beta distributions) and to conduct model diagnostic assessments such as likelihood ratio tests for factor number selection and goodness-of-fit tests for Beta distribution assumptions. Estimation routines encompass maximum likelihood estimation for finite-dimensional Beta factor models, regularized Beta factor analysis for high-dimensional datasets, and shrinkage-based estimation for robust Beta factor loading recovery in noisy or incomplete data environments. The package's methodological framework is detailed in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.00 score

DTFM - Distributed Online Covariance Matrix Tests for Truncated Factor Model

The truncated factor model is a statistical model designed to handle specific data structures in data analysis. 'DTFM' is a powerful tool designed to efficiently process and analyze distributed datasets. The philosophy of the package is described in Guo et al. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.00 score 520 downloads

CoFM - Copula Factor Models

Provides tools for factor analysis in high-dimensional settings under copula-based factor models. It includes functions to simulate factor-model data with copula-distributed idiosyncratic errors (e.g., Clayton, Gumbel, Frank, Student t and Gaussian copulas) and to perform diagnostic tests such as the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity. Estimation routines include principal component based factor analysis, projected principal component analysis, and principal orthogonal complement thresholding for large covariance matrix estimation. The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.00 score 180 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. (2025) <doi:10.1016/j.physa.2024.130308>.

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1.00 score 172 downloads

SDGLM - Scalable Bayesian Inference for Dynamic Generalized Linear Models

Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025).

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1.00 score 154 downloads

DSFM - Distributed Skew Factor Model Estimation Methods

Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

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1.00 score 132 downloads

ELIC - LIC for Distributed Elliptical Model

This comprehensive toolkit for Distributed Elliptical model is designated as "ELIC" (The LIC for Distributed Elliptical Model Analysis) analysis. It is predicated on the assumption that the error term adheres to a Elliptical distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.

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1.00 score 472 downloads

CLIC - The LIC for Distributed Cosine Regression Analysis

This comprehensive framework for periodic time series modeling is designated as "CLIC" (The LIC for Distributed Cosine Regression Analysis) analysis. It is predicated on the assumption that the underlying data exhibits complex periodic structures beyond simple harmonic components. The philosophy of the method is articulated in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.

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1.00 score 3 scripts 184 downloads

SLIC - LIC for Distributed Skewed Regression

This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) <doi:10.1080/02664763.2022.2053949>.

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1.00 score 121 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. (2024) <doi:10.1016/j.ins.2024.121133>.

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1.00 score 1 scripts 241 downloads

Domean - Distributed Online Mean Tests

Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Domean' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean testing. The philosophy of 'Domean' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

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1.00 score 142 downloads

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>.

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1.00 score 190 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>.

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1.00 score 184 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>.

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1.00 score 1 scripts 192 downloads