R Packages

This R package performs the triangulation for any arbitrary polygonal domain [Read more].

Reference:

Lai, M. J. and Wang, L. (2013). Bivariate penalized splines for regression. Statistica Sinica, 23, 1399-1417.

This R package provides the bivariate spline basis functions and implements the bivariate penalized spline smoothing over triangulation in Lai and Wang (2013). [Read more]

Reference:

Lai, M. J. and Wang, L. (2013). Bivariate penalized splines for regression. Statistica Sinica, 23, 1399-1417.

This R package provides the code for estimating and making inference for the generalized geoadditive models in Yu, et al. (2020).

Reference:

Yu, S., Wang, G., Wang, L., Liu, C. and Yang, L. (2020). Estimation and inference for generalized geoadditive models. Journal of the American Statistical Association, Theory and Methods, 115, 761-774.

Wang, L., Wang, G., Lai, M. J. and Gao, L. (2020). Efficient estimation of partially linear models for data on complicated domains by bivariate penalized splines over triangulation. Statistica Sinica, 30, 347-369.

This R package provides the generalized spatially varying coefficient model in Mu, Wang and Wang (2018) and Kim and Wang (2021).

Reference:

Mu, J., Wang, G. and Wang, L. (2018). Estimation and inference in spatially varying coefficient models. Environmetrics, 29:e2485.

Kim, M. and Wang, L. (2021). Generalized spatially varying coefficient models. Journal of Computational and Graphical Statistics, 30, 1-10.

This R package provides the simultaneous confidence corridors (SCC) for mean functions in functional data analysis of imaging data in Wang, et al (2020) .

Reference:

Wang, Y., Wang, G., Wang, L. and Ogden, T. (2020). Simultaneous confidence corridors for mean functions in functional data analysis of imaging data. Biometrics, 76, 427-437.

A reliable and accurate dataset of the cases is vital for scientists to conduct related research and for policymakers to make better decisions. We collect the U.S. COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them.

To obtain reliable data for further analysis, Wang, et al. (2020) examined the cyclical pattern and the following anomalies: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we develop this cdcar R package to provide some anomaly detection and repairing methods if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources which are also essential for understanding the spread of the virus.

For public usage, a Github repository is established to provide daily updated and cleaned data.

Reference:

Wang, G., et al. (2020). Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing. [arXiv: 2006.01333]

Wang, et al. (2021) established a new spatiotemporal epidemic modeling (STEM) framework for space-time infected/death count data to study the dynamic pattern in the spread of COVID-19. The proposed methodology can be used to dissect the spatial structure and dynamics of spread, as well as to assess how this outbreak may unfold through time and space.

Reference:

Wang, L., et al. (2021) Spatiotemporal dynamics, nowcasting and forecasting of COVID-19 in the United States. [arXiv: 2004.14103]