Description
Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour from empirical time series data. One of the main features of chaos is the well known initial value sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify the initial value sensitive property estimating the Lyapunov exponents. The DChaos package provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos based on the Lyapunov exponent in order to know if the data generating process behind time series behave chaotically or not.
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| Flavor | Status |
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| r-devel-linux-x86_64-debian-clang | NOTE |
| r-devel-linux-x86_64-debian-gcc | NOTE |
| r-devel-linux-x86_64-fedora-clang | NOTE |
| r-devel-linux-x86_64-fedora-gcc | NOTE |
| r-devel-macos-arm64 | NOTE |
| r-devel-windows-x86_64 | NOTE |
| r-oldrel-macos-arm64 | NOTE |
| r-oldrel-macos-x86_64 | NOTE |
| r-oldrel-windows-x86_64 | NOTE |
| r-patched-linux-x86_64 | NOTE |
| r-release-linux-x86_64 | NOTE |
| r-release-macos-arm64 | NOTE |
| r-release-macos-x86_64 | NOTE |
| r-release-windows-x86_64 | NOTE |
Check details (14 non-OK)
NOTE
r-devel-linux-x86_64-debian-clang
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-devel-linux-x86_64-debian-gcc
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
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checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-devel-linux-x86_64-fedora-clang
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-devel-linux-x86_64-fedora-gcc
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-devel-macos-arm64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-devel-windows-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-oldrel-macos-arm64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-oldrel-macos-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-oldrel-windows-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-patched-linux-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-release-linux-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-release-macos-arm64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-release-macos-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
NOTE
r-release-windows-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m}. Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.max.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent considering the Norma-2 procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1} (largest). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
checkRd: (-1) lyapunov.spec.Rd:24: Lost braces; missing escapes or markup?
24 | This function returns several objects considering the parameter set selected by the user. The Lyapunov exponent spectrum considering the QR decomposition procedure by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitted values, the residuals obtained, the best embedding parameters set chosen, the sample size or the block length considered by each blocking method. This function provides the standard error, the z test value and the p-value for testing the null hypothesis \eqn{H0: \lambda_k > 0 for k = 1,2,3, \ldots, m} (full spectrum). Reject the null hypothesis ${H_0}$ means lack of chaotic behaviour. That is, the data-generating process does not have a chaotic attractor because of it does not show the property of sensitivity to initial conditions.
| ^
Check History
NOTE 0 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE
r-devel-linux-x86_64-debian-clang
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-devel-linux-x86_64-debian-gcc
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-devel-linux-x86_64-fedora-clang
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-devel-linux-x86_64-fedora-gcc
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-devel-macos-arm64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-devel-windows-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-patched-linux-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-release-linux-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-release-macos-arm64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-release-macos-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-release-windows-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-oldrel-macos-arm64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-oldrel-macos-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
NOTE
r-oldrel-windows-x86_64
Rd files
checkRd: (-1) lyapunov.Rd:57: Lost braces; missing escapes or markup?
57 | This function returns several objects considering the parameter set selected by the user. The largest Lyapunov exponent (Norma-2 procedure) and the Lyapunov exponent spectrum (QR decomposition procedure) by each blocking method are estimated. It also contains some useful information about the estimated jacobian, the best-fitted feed-forward single hidden layer neural net model, the best set of weights found, the fitte
Dependency Network
Version History
new
0.1-7
Mar 9, 2026