Imports: Rdpack, diversitree, mvtnorm, flexsurv, stats, survival,

Title: Higher Criticism Tuned Regression
Authors@R: c(person("Tao", "Jiang", role = c("aut", "cre"),
Description: A novel searching scheme for tuning parameter in high-dimensional
penalized regression. We propose a new estimate of the regularization
parameter based on an estimated lower bound of the proportion of false
The bound is estimated by applying the empirical null distribution of the higher
criticism statistic, a second-level significance testing, which is constructed
by dependent p-values from a multi-split regression and aggregation method
of tuning parameter in penalized regression is decided corresponding to the lower
bound of the proportion of false null hypotheses. Different penalized
regression methods are provided in the multi-split algorithm.
Imports: glmnet (>= 2.0-18), harmonicmeanp (>= 3.0), MASS, ncvreg (>=

Title: Hierarchical Clustering from Vertex-Links
Description: Hierarchical clustering for spatial data, which requires clustering
results not only homogeneous in non-geographical features among samples but
also geographically close to each other within a cluster. It modified typically
used hierarchical agglomerative clustering algorithms for introducing the spatial
homogeneity, by considering geographical locations as vertices and converting
spatial adjacency into whether a shared edge exists between a pair of vertices
(Tzeng & Hsu, 2022) <arXiv:2201.08302>. The constraints of the vertex links
automatically enforce the spatial contiguity property at each step of iterations.
In addition, methods to find an appropriate number of clusters and to report
Imports: BLSM (>= 0.1.0), cluster, geometry (>= 0.4.5), graphics,
grDevices, M3C (>= 1.12.0), MASS, Matrix, rgeos (>= 0.5.1), sp
Suggests: alphahull, knitr, fields (>= 11.4)
Authors@R: c(person("ShengLi", "Tzeng", email = "slt.cmu@gmail.com", role=c("cre","aut")),
person("Hao-Yun", "Hsu", email = "zoez5230100@gmail.com", role="aut"))

Title: Regression with a Large Number of Potential Explanatory
Maintainer: H. H. Hoeltgebaum <hh3015@ic.ac.uk>
Description: Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.