Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Implementation of selected 'Tidyverse' functions within 'DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the serverside package which should be installed on the server holding the data, and is used in conjuncture with the clientside package 'dsTidyverseClient' which is installed in the local R environment of the analyst. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.

Implementation of selected 'Tidyverse' functions within 'DataSHIELD', an open-source federated analysis solution in R. Currently, 'DataSHIELD' contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the clientside package which should be installed locally, and is used in conjuncture with the serverside package 'dsTidyverse' which is installed on the remote server holding the data. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.

Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.