Title: Downloads and processes GHCN daily
Depends: R (>= 2.11.0),R.utils, R.oo, R.methodsS3, abind, ncdf
Description: The GhcnDaily package provides the core functions required
to download and format the GHCN Daily Data into Monthly
datasets. It is currently restricted to operating with

Depends: R (>= 2.11.0), R.utils, R.oo, R.methodsS3, abind, ncdf

to download and format the GHCN Daily Data (TMax and Tmin) into
Monthly datasets. It is currently restricted to operating with

Description: The GhcnDaily  package provides the core functions required to
download and format the GHCN Daily Data (TMax and Tmin) into Monthly datasets. It is
currently restricted to operating with temperature data from the daily files.

Title: Missing Data Segments Imputation in Multivariate Streams
Author: Siyavash Shabani, Reza Rawassizadeh
Description: Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.

Title: The Knockoff Inference Using Summary Statistics
Description: Functions for multiple knockoff inference using summary statistics, e.g. Z-scores. The knockoff inference is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. This package provides a procedure which performs knockoff inference without ever constructing individual knockoffs (GhostKnockoff). It additionally supports multiple knockoff inference for improved stability and reproducibility. Moreover, it supports meta-analysis of multiple overlapping studies.
Depends: R(>= 3.5.0), Matrix, CVXR, Rdsdp, gtools, seqminer