High-Dimensional Changepoint Estimation via Sparse Projection
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.

Tengyao Wang, Bertille Follain and Richard Samworth

c(
person("Maarten", "Blaauw", email = "maarten.blaauw@qub.ac.uk", role = c("aut", "cre")))
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.

c(
person("Maarten", "Blaauw", email = "maarten.blaauw@qub.ac.uk", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5680-1515")))
Maarten Blaauw [aut, cre] (<https://orcid.org/0000-0002-5680-1515>)

Several integrative data methods in which information of objects from different data sources can be combined are included in the IntClust package. As a single data source is limited in its point of view, this provides more insight and the opportunity to investigate how the variables are interconnected. Clustering techniques are to be applied to the combined information. For now, only agglomerative hierarchical clustering is implemented. Further, differential gene expression and pathway analysis can be conducted on the clusters. Plotting functions are available to visualize and compare results of the different methods.
ade4,a4Core, Biobase, cluster, plotrix, plyr, gplots,
gridExtra, limma, prodlim,
gtools,e1071,pls,stats,utils,graphics,FactoMineR,analogue,lsa,
SNFtool,grDevices,ggplot2