Predicts the occurrence times (in day-of-year) of spring phenological events. Three methods, including the accumulated degree days (ADD) method, the accumulated days transferred to a standardized temperature (ADTS) method, and the accumulated developmental progress (ADP) method, were used. See Shi et al. (2017a) <doi:10.1016/j.agrformet.2017.04.001> and Shi et al. (2017b) <doi:10.1093/aesa/sax063> for details.

The developed package can be used to generate a spatial population for different
             levels of relationships among the dependent and auxiliary variables along with spatially
             varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations
             are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal
             and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<doi:10.1080/10485252.2018.1499907>.
             The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the 
             spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially 
             generated data.

Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.

Sparse arrays interpreted as multivariate polynomials.
    Uses 'disordR' discipline (Hankin, 2022,
    <doi:10.48550/ARXIV.2210.03856>).  To cite the package in
    publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.

The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.