Packaged: 2018-03-05 20:41:31 UTC; mrodriguez

Packaged: 2018-05-29 16:57:35 UTC; mrodriguez

Packaged: 2018-11-11 18:12:00 UTC; mrodriguez

Authors@R: person("Yun", "Cai", email = "Yun.Cai@dal.ca", role = c("aut", "cre"))
Description: Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. When we are interested in the differences between the structures of two groups of communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney (2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while retaining all the advantages of NMF -- such as interpretability, and being based on a simple biological intuition.

Title: Linear Network Preprocessing for Spatial Statistics
Description: Launches a Shiny application that allows users to carry out some of the steps that are required to curate both a linear network object based on a road structure and a point pattern that lies on such a network, becoming two previous steps to the performance of a spatial statistics analysis.
Imports: spatstat, leaflet, shiny, sp, raster, maptools

Imports: spatstat, leaflet, shiny, sp, raster, maptools, rgdal,

Imports: spatstat.geom, spatstat.core, spatstat.linnet, spatstat (>=
2.0-0), leaflet, shiny, sp, raster, maptools, rgdal,

Title: Nonparametric Modeling and Monitoring of Spatio-Temporal Data
Authors@R: c(person("Kai",  "Yang", role = c("aut", "cre"), email = "kayang@mcw.edu"),
Description: Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2022) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.