progress, rgl, robustbase, scales, nleqslv

Title: Bootstrap Inference for Network COstruction
Author: Shuang Li <shuangli@fhcrc.org>,Li Hsu <lih@fhcrc.org>, Jie Peng
Description: Network construction based on inference on edge selection
frequencies generated from bootstrap resamples

Title: Estimate the Correlation Between Two Irregular Climate Time
Authors@R: c(person("Josué M.", "Polanco-Martínez", role = c("aut",
"cph", "cre"), email = "josue.m.polanco@gmail.com"),
person("Mikko", "Korpela", role = c("ctb", "trl")),
person("Aalto University", role = c("cph")),
person("Manfred", "Mudelsee", role = "ctb"))
Author: Josué M. Polanco-Martínez [aut, cph, cre],
Copyright: Authors and file inst/COPYRIGHTS
Maintainer: Josué M. Polanco-Martínez <josue.m.polanco@gmail.com>
Description: Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two time series with different timescales. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).

Title: Estimate the Correlation Between Two Irregular Time Series
Description: Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).