RTransferEntropy

Introduction to RTransferEntropy

The measurement of information transfer between different time series is the basis of research questions in various research areas, including biometrics, economics, ecological modelling, neuroscience, sociology, and thermodynamics. The quantification of information transfer commonly relies on measures that have been derived from subject-specific assumptions and restrictions concerning the underlying stochastic processes or theoretical models. With the development of transfer entropy, information theory based measures have become a popular alternative to quantify information flows within various disciplines. Transfer entropy is a non-parametric measure of directed, asymmetric information transfer between two processes.

We show how to quantify the information flow between two stationary time series and how to test for its statistical significance using Shannon transfer entropy and Rényi transfer entropy within the package RTransferEntropy. A core aspect of the provided package is to allow statistical inference and hypothesis testing in the context of transfer entropy. To this end, we first present the methodology, i.e. the derivation and calculation of transfer entropy as well as the associated bias correction applied to calculate effective transfer entropy, and describe our approach to statistical inference. Afterwards, we introduce the package in detail and demonstrate its functionality in several applications to simulated processes as well as an application to financial time series.

Measuring information flows using transfer entropy

Let log denote the logarithm to the base 2, then informational gain is measured in bits. Shannon entropy (Shannon 1948) states that for a discrete random variable J with probability distribution p(j), where j stands for the different outcomes the random variable J can take, the average number of bits required to optimally encode independent draws from the distribution of J can be calculated as

H_J = −∑_jp(j) ⋅ log(p(j)).

Strictly speaking, Shannon’s formula is a measure for uncertainty, which increases with the number of bits needed to optimally encode a sequence of realizations of J. In order to measure the information flow between two processes, Shannon entropy is combined with the concept of the Kullback-Leibler distance (Kullback and Leibler 1951) and by assuming that the underlying processes evolve over time according to a Markov process (Schreiber 2000). Let I and J denote two discrete random variables with marginal probability distributions p(i) and p(j) and joint probability distribution p(i, j), whose dynamical structures correspond to stationary Markov processes of order k (process I) and l (process J). The Markov property implies that the probability to observe I at time t + 1 in state i conditional on the k previous observations is p(i_t + 1|i_t, ..., i_{t − k + 1}) = p(i_t + 1|i_t, ..., i_t − k). The average number of bits needed to encode the observation in t + 1 if the previous k values are known is given by

h_I(k) = −∑_ip(i_t + 1, i_t^(k)) ⋅ log(p(i_t + 1|i_t^(k))), where i_t^(k) = (i_t, ..., i_{t − k + 1}). h_J(l) can be derived analogously for process J. In the bivariate case, information flow from process J to process I is measured by quantifying the deviation from the generalized Markov property p(i_t + 1|i_t^(k)) = p(i_t + 1|i_t^(k), j_t^(l)) relying on the Kullback-Leibler distance (Schreiber 2000). Thus, (Shannon) transfer entropy is given by

$$ T_{J \rightarrow I}(k,l) = \sum_{i,j} p\left(i_{t+1}, i_t^{(k)}, j_t^{(l)}\right) \cdot log \left(\frac{p\left(i_{t+1}| i_t^{(k)}, j_t^{(l)}\right)}{p\left(i_{t+1}|i_t^{(k)}\right)}\right), $$ where T_J → I consequently measures the information flow from J to I ( T_I → J as a measure for the information flow from I to J can be derived analogously).

Transfer entropy can also be based on Rényi entropy (Rényi 1970) rather than Shannon entropy. Rényi entropy introduces a weighting parameter q > 0 for the individual probabilities p(j) and can be calculated as $$ H^q_J = \frac{1}{1-q} log \left(\sum_j p^q(j)\right). $$ For q → 1, Rényi entropy converges to Shannon entropy. For 0 < q < 1 events that have a low probability to occur receive more weight, while for q > 1 the weights induce a preference for outcomes j with a higher initial probability. Consequently, Rényi entropy provides a more flexible tool for estimating uncertainty, since different areas of a distribution can be emphasized, depending on the parameter q.

Using the escort distribution (for more information, see Beck and Schögl 1993) $\phi_q(j)=\frac{p^q(j)}{\sum_j p^q(j)}$ with q > 0 to normalize the weighted distributions, Jizba, Kleinert, and Shefaat (2012) derive the Rényi transfer entropy measure as $$ RT_{J \rightarrow I}(k,l) = \frac{1}{1-q} log \left(\frac{\sum_i \phi_q\left(i_t^{(k)}\right)p^q\left(i_{t+1}|i^{(k)}_t\right)}{\sum_{i,j} \phi_q\left(i^{(k)}_t,j^{(l)}_t\right)p^q\left(i_{t+1}|i^{(k)}_t,j^{(l)}_t \right)}\right). $$ Analogously to (Shannon) transfer entropy, Rényi transfer entropy measures the information flow from J to I. Note that, contrary to Shannon transfer entropy, the calculation of Rényi transfer entropy can result in negative values. In such a situation, knowing the history of J reveals even greater risk than would otherwise be indicated by only knowing the history of I alone. For more details on this issue see Jizba, Kleinert, and Shefaat (2012).

The above transfer entropy estimates are commonly biased due to small sample effects. A remedy is provided by the effective transfer entropy (Marschinski and Kantz 2002), which is computed in the following way:

ET_J → I(k, l) = T_J → I(k, l) − T_{Jshuffled → I}(k, l), where T_{Jshuffled → I}(k, l) indicates the transfer entropy using a shuffled version of the time series of J. Shuffling implies randomly drawing values from the time series of J and realigning them to generate a new time series. This procedure destroys the time series dependencies of J as well as the statistical dependencies between J and I. As a result T_{Jshuffled → I}(k, l) converges to zero with increasing sample size and any nonzero value of T_{Jshuffled → I}(k, l) is due to small sample effects. The transfer entropy estimates from shuffled data can therefore be used as an estimator for the bias induced by these small sample effects. To derive a consistent estimator, shuffling is repeated many times and the average of the resulting shuffled transfer entropy estimates across all replications is subtracted from the Shannon or Rényi transfer entropy estimate to obtain a bias corrected effective transfer entropy estimate.

In order to assess the statistical significance of transfer entropy estimates, we rely on a Markov block bootstrap as proposed by Dimpfl and Peter (2013). In contrast to shuffling, the Markov block bootstrap preserves the dependencies within each time series. Thereby, it generates the distribution of transfer entropy estimates under the null hypothesis of no information transfer, i.e. randomly drawn blocks of process J are realigned to form a simulated series, which retains the univariate dependencies of J but eliminates the statistical dependencies between J and I. Shannon or Rényi transfer entropy is then estimated based on the simulated time series. Repeating this procedure yields the distribution of the transfer entropy estimate under the null of no information flow. The p-value associated with the null hypothesis of no information transfer is given by 1 − q̂_TE, where q̂_TE denotes the quantile of the simulated distribution that corresponds to the original transfer entropy estimate.

The calculation of Shannon and Rényi transfer entropy is based on discrete data. If the data does not exhibit a discrete structure that allows for transfer entropy estimation, it has to be discretized. This can be achieved by symbolic recoding, i.e. by partitioning the data into a finite number of bins, which can either be based on defining upper and lower bounds for the bins a priori or by choosing specific quantiles of the empirical distribution of the data. Denote the bounds specified for the n bins by q₁, q₂, ..., q_n, where q₁ < q₂ < ... < q_n, and consider a time series denoted by y_t, the data is recoded as

$$ S_t= \begin{cases} ~1~~~~~~~~ \mbox{ for }~ y_t\leq q_1\\ ~ 2~ ~~~~~~~\mbox{ for }~ q_1<y_t\leq q_2\\ ~\vdots~~~~~~~~~~~~~~~~~\vdots\\ ~n-1~~\mbox{ for }~ q_{n-1}<y_t \leq q_n\\ ~n ~~~~~~~~\mbox{ for } ~ y_t\geq q_n \end{cases}. $$ Thereby, each value in the observed time series y_t is replaced by an integer (1,2,…,n), according to how S_t relates to the interval specified by the lower and upper bounds q₁ to q_n. The choice of the bins should be motivated by the distribution of the data. However, we recommend that the number of bins is limited in order to avoid too many zero observations when calculating relative frequencies as estimators of the joint probabilities in the (effective) transfer entropy equations.

The RTransferEntropy package

Testing for and quantifying the information flow between two time series with Shannon and Rényi transfer entropy, as outlined above, can be implemented with the package RTransferEntropy. The package is installed and loaded in the usual way.

# Install from CRAN
install.packages('RTransferEntropy')
# Install development version from GitHub
# devtools::install_github("BZPaper/RTransferEntropy")

# load the package
library(RTransferEntropy)

The main function is transfer_entropy(), which creates an object of class transfer_entropy that contains the respective transfer entropy estimates (both in J → I and I → J direction), the related effective transfer entropy estimates, standard errors, and p-values for the estimated values, an indication of statistical significance, and quantiles of the bootstrap samples (if the number of bootstrap replications is specified to be greater than zero). Some auxilliary functions are written in C++ using Rcpp to speed up computations. Furthermore, we use the future package to enable parallel computing, which allows for more efficient programming when the transfer_entropy() function is called repeatedly or when large amounts of shuffles and bootstraps are used.

transfer_entropy(x, y, 
                 lx = 1, ly = 1, q = 0.1, 
                 entropy = c('Shannon', 'Renyi'), shuffles = 100, 
                 type = c('quantiles', 'bins', 'limits'),
                 quantiles = c(5, 95), bins = NULL, limits = NULL,
                 nboot = 300, burn = 50, quiet = FALSE, seed = NULL)

set.seed(12345)
n <- 2500
x <- rep(0, n + 1)
y <- rep(0, n + 1)

for (i in 2:(n + 1)) {
  x[i] <- 0.2 * x[i - 1] + rnorm(1, 0, 2)
  y[i] <- x[i - 1] + rnorm(1, 0, 2)
}

x <- x[-1]
y <- y[-1]

library(future)
# enable parallel processing for all future transfer_entropy calls
# use multicore on unix machines for better performance
plan(multisession)

#> 
#> Attaching package: 'future'
#> The following object is masked from 'package:rmarkdown':
#> 
#>     run

set.seed(12345)
shannon_te <- transfer_entropy(x, y)
#> Shannon's entropy on 4 cores with 100 shuffles.
#>   x and y have length 2500 (0 NAs removed)
#>   [calculate] X->Y transfer entropy
#>   [calculate] Y->X transfer entropy
#>   [bootstrap] 300 times
#> Done - Total time 7.51 seconds

shannon_te
#> Shannon Transfer Entropy Results:
#> -----------------------------------------------------------
#> Direction        TE   Eff. TE  Std.Err.   p-value    sig
#> -----------------------------------------------------------
#>      X->Y    0.0933    0.0901    0.0013    0.0000    ***
#>      Y->X    0.0025    0.0000    0.0013    0.7100       
#> -----------------------------------------------------------
#> Bootstrapped TE Quantiles (300 replications):
#> -----------------------------------------------------------
#> Direction      0%     25%     50%     75%    100%
#> -----------------------------------------------------------
#>      X->Y  0.0009  0.0023  0.0030  0.0039  0.0084
#>      Y->X  0.0008  0.0024  0.0030  0.0039  0.0106
#> -----------------------------------------------------------
#> Number of Observations: 2500
#> -----------------------------------------------------------
#> p-values: < 0.001 '***', < 0.01 '**', < 0.05 '*', < 0.1 '.'

# X->Y
calc_te(x, y)
#> [1] 0.09331606
calc_ete(x, y)
#> [1] 0.09022173

# and Y->X
calc_te(y, x)
#> [1] 0.002455513
calc_ete(y, x)
#> [1] 0

set.seed(12345)
n <- 2500
x <- rep(0, n + 200)
y <- rep(0, n + 200)

x[1] <- rnorm(1, 0, 1)
y[1] <- rnorm(1, 0, 1)

for (i in 2:(n + 200)) {
  x[i] <- 0.2 * x[i - 1] + rnorm(1, 0, 1)
  y[i] <- sqrt(abs(x[i - 1])) + rnorm(1, 0, 1)
}

x <- x[-(1:200)]
y <- y[-(1:200)]

shannon_te2 <- transfer_entropy(x, y)
#> Shannon's entropy on 4 cores with 100 shuffles.
#>   x and y have length 2500 (0 NAs removed)
#>   [calculate] X->Y transfer entropy
#>   [calculate] Y->X transfer entropy
#>   [bootstrap] 300 times
#> Done - Total time 7.35 seconds

shannon_te2
#> Shannon Transfer Entropy Results:
#> -----------------------------------------------------------
#> Direction        TE   Eff. TE  Std.Err.   p-value    sig
#> -----------------------------------------------------------
#>      X->Y    0.0147    0.0111    0.0011    0.0000    ***
#>      Y->X    0.0032    0.0000    0.0012    0.4167       
#> -----------------------------------------------------------
#> Bootstrapped TE Quantiles (300 replications):
#> -----------------------------------------------------------
#> Direction      0%     25%     50%     75%    100%
#> -----------------------------------------------------------
#>      X->Y  0.0011  0.0026  0.0033  0.0041  0.0069
#>      Y->X  0.0011  0.0023  0.0030  0.0038  0.0076
#> -----------------------------------------------------------
#> Number of Observations: 2500
#> -----------------------------------------------------------
#> p-values: < 0.001 '***', < 0.01 '**', < 0.05 '*', < 0.1 '.'

library(vars)
varfit <- VAR(cbind(x, y), p = 1, type = "const")
svf <- summary(varfit)

svf$varresult$y
#> 
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -3.6282 -0.6811  0.0170  0.6941  3.5409 
#> 
#> Coefficients:
#>        Estimate Std. Error t value Pr(>|t|)    
#> x.l1  -0.004120   0.020498  -0.201    0.841    
#> y.l1   0.006781   0.020016   0.339    0.735    
#> const  0.795104   0.026262  30.276   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 1.041 on 2496 degrees of freedom
#> Multiple R-squared:  6.218e-05,  Adjusted R-squared:  -0.000739 
#> F-statistic: 0.07761 on 2 and 2496 DF,  p-value: 0.9253

df <- data.frame(q1 = 5:25, q2 = 95:75)

df$ete <- apply(
  df, 1,
  function(el) calc_ete(x, y, quantiles = c(el[["q1"]], el[["q2"]]))
)

df$quantiles <- factor(sprintf("(%02.f, %02.f)", df$q1, df$q2))

ggplot(df, aes(x = quantiles, y = ete)) + 
  geom_point() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(x = "Quantiles", y = "ETE (X->Y)")

set.seed(12345)

x <- rep(0, n + 200)
y <- rep(0, n + 200)

x[1] <- rnorm(1, 0, 1)
y[1] <- rnorm(1, 0, 1)

for (i in 2:(n + 200)) {
  x[i] <- 0.2 * x[i - 1] + rnorm(1, 0, 1)
  y[i] <- ifelse(
    abs(x[i - 1]) > 1.65,
    x[i - 1]  + rnorm(1, 0, 1),
    0.2 * x[i - 1] + rnorm(1, 0, 1)
  )
}

x <- x[-(1:200)]
y <- y[-(1:200)]

set.seed(12345)
renyi_te <- transfer_entropy(x, y, entropy = "Renyi", q = 0.3)
#> Renyi's entropy on 4 cores with 100 shuffles.
#>   x and y have length 2500 (0 NAs removed)
#>   [calculate] X->Y transfer entropy
#>   [calculate] Y->X transfer entropy
#>   [bootstrap] 300 times
#> Done - Total time 7.05 seconds

renyi_te
#> Renyi Transfer Entropy Results:
#> -----------------------------------------------------------
#> Direction        TE   Eff. TE  Std.Err.   p-value    sig
#> -----------------------------------------------------------
#>      X->Y    0.1889    0.0584    0.0385    0.0333      *
#>      Y->X    0.0523   -0.0610    0.0393    0.9367       
#> -----------------------------------------------------------
#> Bootstrapped TE Quantiles (300 replications):
#> -----------------------------------------------------------
#> Direction      0%     25%     50%     75%    100%
#> -----------------------------------------------------------
#>      X->Y  0.0028  0.0946  0.1203  0.1471  0.2320
#>      Y->X  0.0026  0.0842  0.1077  0.1387  0.2191
#> -----------------------------------------------------------
#> Number of Observations: 2500
#> Q: 0.3
#> -----------------------------------------------------------
#> p-values: < 0.001 '***', < 0.01 '**', < 0.05 '*', < 0.1 '.'

qs <- c(seq(0.1, 0.9, 0.1), 0.99)

te <- sapply(qs, function(q) calc_te(x, y, entropy = "renyi", q = q))
names(te) <- sprintf("q = %.2f", qs)

te_shannon <- calc_te(x, y)
te_shannon
#> [1] 0.1485303

round(te, 4)
#> q = 0.10 q = 0.20 q = 0.30 q = 0.40 q = 0.50 q = 0.60 q = 0.70 q = 0.80 
#>   0.2910   0.2327   0.1889   0.1594   0.1429   0.1364   0.1365   0.1402 
#> q = 0.90 q = 0.99 
#>   0.1447   0.1482

text_df <- data.frame(x = 0.25, 
                      y = te_shannon, 
                      lab = sprintf("Shannon's TE = %.4f", te_shannon))

ggplot(data.frame(x = qs, y = te), aes(x = x, y = y)) +
  geom_hline(yintercept = te_shannon, color = "red", linetype = "dashed") +
  geom_smooth(se = F, color = "black", size = 0.5) +
  theme_light() +
  labs(x = "Values for q", y = "Renyi's Transfer Entropy",
       title = "Renyi's Transfer Entropy for different Values of q") +
  geom_text(data = text_df,
            aes(label = lab), color = "red", nudge_y = 0.01)

Application to financial time series

The analysis of information flows in financial markets has a long history. Using transfer entropy widens the possibilities to detect information flows as nonlinear relationships can also be accounted for. To illustrate the application of transfer entropy, we use a dataset of 10 individual stocks comprised in the S&P 500 index as well as the index itself. The data range from January 3, 2000, to December 29, 2017. This dataset is included as the stocks object in the package.

A stock market index is a weighted average of individual stocks. Of course, small stocks have less weight and, hence, may only have limited impact on the index while large corporations might dominate. On the other hand, it is unclear upfront how the market environment as a whole (as measured by the index) might provide information to the individual stocks. To measure the extent to which information flows between the index and the stocks, we use transfer entropy. As the calculation of transfer entropy requires stationary data, we calculate log-returns from the price series.

library(data.table) # for data manipulation

res <- lapply(split(stocks, stocks$ticker), function(d) {
  te <- transfer_entropy(d$ret, d$sp500, shuffles = 50, nboot = 100, quiet = T)
  
  data.table(
    ticker = d$ticker[1],
    dir = c("X->Y", "Y->X"),
    coef(te)[1:2, 2:3]
  )
})

df <- rbindlist(res)

# order the ticker by the ete of X->Y
df[, ticker := factor(ticker, 
                      levels = unique(df$ticker)[order(df[dir == "X->Y"]$ete)])]

# rename the variable (xy/yx)
df[, dir := factor(dir, levels = c("X->Y", "Y->X"),
                   labels = c("Flow towards Market",
                              "Flow towards Stock"))]

ggplot(df, aes(x = ticker, y = ete)) + 
  facet_wrap(~dir) +
  geom_hline(yintercept = 0, color = "gray") +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(x = NULL, y = "Effective Transfer Entropy") +
  geom_errorbar(aes(ymin = ete - qnorm(0.95) * se,  
                    ymax = ete + qnorm(0.95) * se),  
                width = 0.25, col = "blue") +
  geom_point()

The graphic reports effective transfer entropy estimates together with 95% confidence bounds for 10 stocks. The results illustrate that there is indeed bi-directional information flow. However, the information flow from the stocks to the market is higher for most stocks than the other direction. Also, as can easily be seen from the confidence bounds, the information flow towards the stock is not statistically significant in two cases (on a 5% significance level).

Again, the estimated transfer entropy depends on the choice of the quantile. This is illustrated in the following Figure which reports the changes of effective transfer entropy values for two different quantiles (05, 95) and c(10, 90) for the two flow directions: stock towards market on the left facet, and market towards stock on the right facet. The different colors represent different stocks. The estimated information flow from the market to the stocks is rather robust with respect to the choice of the quantiles, most values in both flow directions show little change (as indicated by small absolute slopes of the lines).

# calculate the same ete with different quantiles
df2 <- stocks[, .(ete_xy = calc_ete(ret, sp500, quantiles = c(10, 90)),
                  ete_yx = calc_ete(sp500, ret, quantiles = c(10, 90))), 
              by = ticker]

# combine the quantiles into a single dt

df1 <- dcast(df[, .(dir, ticker, ete)], ticker ~ dir, value.var = "ete")
setnames(df1, c("ticker", "ete_xy", "ete_yx"))
dt <- rbindlist(list(
  df1[, quantiles := "(05, 95)"],
  df2[, quantiles := "(10, 90)"]
))

df_long2 <- melt(dt, id.vars = c("ticker", "quantiles"))

df_long2[, quantiles := factor(quantiles, levels = c("(05, 95)", "(10, 90)"))]
df_long2[, variable := factor(variable, levels = c("ete_xy", "ete_yx"),
                              labels = c("Flow towards Market",
                                         "Flow towards Stock"))]

ggplot(df_long2, aes(x = quantiles, y = value, color = ticker, group = ticker)) + 
  geom_line() + 
  facet_wrap(~variable) +
  labs(
    x = "Quantiles", 
    y = "Effective Transfer Entropy",
    title = "Change of ETE-Values for different Quantiles",
    color = "Ticker"
  )

In finance, pricing relevant information is readily associated with tail events, i.e. relatively large positive or negative returns. If these are indeed more relevant, Rényi transfer entropy provides a tool to give more weight to their contribution to the overall information flow. To illustrate this feature we use one stock and calculate Rényi transfer entropy from the index to the stock for a selection of the weighting parameters q.

qs <- c(seq(0.1, 0.9, 0.1), 0.99)
d <- stocks[ticker == "AXP"]

q_list <- lapply(qs, function(q) {
  
  # transfer_entropy will give a warning as nboot < 100
  suppressWarnings({
    tefit <- transfer_entropy(d$ret, d$sp500, lx = 1, ly = 1, 
                              entropy = "Renyi", q = q, 
                              shuffles = 50, quantiles = c(10, 90), 
                              nboot = 20, quiet = T)
  })
  data.table(
    q   = q,
    dir = c("X->Y", "Y->X"),
    coef(tefit)[, 2:3]
  )
})
qdt <- rbindlist(q_list)

sh_dt <- data.table(
  dir = c("X->Y", "Y->X"),
  ete = c(calc_ete(d$ret, d$sp500), calc_ete(d$sp500, d$ret))
)
qdt[, pe := qnorm(0.95) * se]

ggplot(qdt, aes(x = q, y = ete)) +
  geom_hline(yintercept = 0, color = "darkgray") + 
  geom_hline(data = sh_dt, aes(yintercept = ete), linetype = "dashed",
             color = "red") +
  geom_point() +
  geom_errorbar(aes(ymin = ete - pe, ymax = ete + pe), 
                width = 0.25/10, col = "blue") +
  facet_wrap(~dir) +
  labs(x = "Values for q", y = "Renyi's Transfer Entropy",
       title = "Renyi's Transfer Entropy for different Values of q",
       subtitle = "For American Express (AXP, X) and the S&P 500 Index (Y)")

For low values of q, the information in the tails is given a high weight which leads in the current situation to a significant effective transfer entropy result. This indicates that indeed tail dependence is given between the S&P 500 and the stock. As the weight is reduced, the effective transfer entropy decreases and even becomes negative (between q = 0.3 and $q=0.8 in the direction from the stock to the index and between q = 0.5 and q = 0.9 in the other direction). This would mean that the knowledge of the either the stock or the index would even indicate a higher risk expose of the respective other entity. Note that this does not mean that there is no information flow. The information flow merely suggests a higher risk of the dependent variable as opposed to a situation with positive Rényi transfer entropy where the risk about the dependent variable is reduced by knowledge of the other variable.

The red dashed line in the graph represents the Shannon entropy. The last value of q is 0.99 for which the Rényi transfer entropy is already fairly close to the value of the Shannon transfer entropy. This illustrates that Rényi transfer entropy converges to Shannon transfer entropy as q approaches 1.

Parallel execution

RTransferEntropy uses the future package internally that allows for parallel execution. To enable parallel execution, you have to select a plan (see also ?future::plan). The following code uses multiple cores as set by plan(multisession) then it reverts to sequential execution.

library(future)

# enable parallelism
plan(multisession)
te <- transfer_entropy(x, y, nboot = 100)

# execute sequential again
plan(sequential)
te <- transfer_entropy(x, y, nboot = 100)

Turning the function to quiet = TRUE might further help to reduce time when the function is called repeatedly. If you want to disable output for all calls, you can use set_quiet(TRUE).

set_quiet(TRUE)
te <- transfer_entropy(x, y, nboot = 0)

set_quiet(FALSE)
te <- transfer_entropy(x, y, nboot = 0)
#> Shannon's entropy on 4 cores with 100 shuffles.
#>   x and y have length 2500 (0 NAs removed)
#>   [calculate] X->Y transfer entropy
#>   [calculate] Y->X transfer entropy
#> Done - Total time 0.16 seconds

# close multisession, see also ?plan
plan(sequential)

Comparison with existing package

The authors of this package are aware of one other package that calculates transfer entropy, the TransferEntropy-package. This package allows the user to compute the Shannon transfer entropy measure using the computeTE()-function and returns a single value for the calculated transfer entropy. The present package also provides the computation of Shannon transfer entropy, but is not limited to it. By default (using the transfer_entropy-function), it computes transfer entropy, effective transfer entropy (Eff. TE), standard errors, and p-values for both directions between the input time series. Standard errors are based on a bootstrap and bootstrapped transfer entropy quantiles are also provided by default. The focus of this package, therefore, is to allow the researcher to conduct inference about hypotheses regarding effective transfer entropies.

Furthermore, the present package also allows the calculation of Rényi transfer entropy. The latter is particularly useful if the tails are assumed to be more informative than the centre of the distribution.

The important difference between the TransferEntropy-package and this package is the way the data are discretized. The former package relies on the k-nearest neighbor approach of Kraskov to estimate mutual information. This package uses symbolic encoding based on selected bins or quantiles of the empirical distribution of the data to estimate empirical frequencies.

As in particular the bootstrap is computationally intensive, this package uses Rcpp and parallel processing to decrease computing time.