Estimate the Correlation Between Two Irregular Time Series
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).

Bayesian BIN (Bias, Information, Noise) Model of Forecasting
c(person(given = "Ville",
family = "Satopää",
role = c("aut", "cre"),
email = "ville.satopaa@gmail.com",
comment = c(ORCID = "https://orcid.org/0000-0002-3787-7303")),
person(given = "Marat",
family = "Salikhov",
role = "aut"),
person(given = "Elvira",
family = "Moreno",
role = "aut"))
A recently proposed Bayesian BIN model disentangles the underlying processes
that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into
three components: bias, partial information, and noise. By describing the differences between two
groups of forecasters, the model allows the user to carry out useful inference, such as calculating
the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information.
It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient
extraction of valid information from the environment improves forecasting accuracy. This package provides
easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov,
Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN
Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
testthat, knitr, rmarkdown, pacman (>= 0.5.1), rstantools (>=
2.1.1), RcppParallel (>= 5.0.1)
Ville Satopää [aut, cre] (<https://orcid.org/0000-0002-3787-7303>),
Marat Salikhov [aut],
Elvira Moreno [aut]