Package: EMLI 0.3.0
EMLI: Computationally Efficient Maximum Likelihood Identification of Linear Dynamical Systems
Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms.
Authors:
EMLI_0.3.0.tar.gz
EMLI_0.3.0.zip(r-4.7-any)EMLI_0.3.0.zip(r-4.6-any)EMLI_0.3.0.zip(r-4.5-any)
EMLI_0.3.0.tgz(r-4.6-any)EMLI_0.3.0.tgz(r-4.5-any)
EMLI_0.3.0.tar.gz(r-4.7-any)EMLI_0.3.0.tar.gz(r-4.6-any)
EMLI_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
EMLI/json (API)
| # Install 'EMLI' in R: |
| install.packages('EMLI', repos = c('https://vy-du.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:a303cef07b. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 104 | ||
| source / vignettes | OK | 155 | ||
| linux-release | OK | 108 | ||
| macos-release | OK | 158 | ||
| macos-oldrel | OK | 219 | ||
| windows-devel | OK | 51 | ||
| windows-release | OK | 52 | ||
| windows-oldrel | OK | 58 | ||
| wasm-release | OK | 85 |
Exports:calculate_likelihoodestimate_parametersestimate_parameters_onevaluate_estimatesgenerate_data
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| calculate_likelihood | calculate_likelihood |
| estimate_parameters | estimate_parameters |
| estimate_parameters_on | estimate_parameters_on |
| evaluate_estimates | evaluate_estimates |
| generate_data | generate_data |