We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.

Mapper-based survival analysis with transcriptomics data is designed to carry out. 
			Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) 
			where survival data is taken into account in the filtering function. More 
			details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, 
			A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap 
			identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics
			and methylation studies" <doi:10.1101/2022.09.08.507080>.

Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) <doi:10.1063/1.4724118>.

Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.

Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.  See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.