Package: slm 1.2.0
slm: Stationary Linear Models
Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arxiv:1906.06583>.
Authors:
slm_1.2.0.tar.gz
slm_1.2.0.zip(r-4.5)slm_1.2.0.zip(r-4.4)slm_1.2.0.zip(r-4.3)
slm_1.2.0.tgz(r-4.4-any)slm_1.2.0.tgz(r-4.3-any)
slm_1.2.0.tar.gz(r-4.5-noble)slm_1.2.0.tar.gz(r-4.4-noble)
slm_1.2.0.tgz(r-4.4-emscripten)slm_1.2.0.tgz(r-4.3-emscripten)
slm.pdf |slm.html✨
slm/json (API)
# Install 'slm' in R: |
install.packages('slm', repos = c('https://e-caron.r-universe.dev', 'https://cloud.r-project.org')) |
- shan - PM2.5 Data of Shanghai
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:7aafbca19d. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 22 2024 |
R-4.5-win | OK | Nov 22 2024 |
R-4.5-linux | OK | Nov 22 2024 |
R-4.4-win | OK | Nov 22 2024 |
R-4.4-mac | OK | Nov 22 2024 |
R-4.3-win | OK | Nov 22 2024 |
R-4.3-mac | OK | Nov 22 2024 |
Exports:cov_ARcov_efromovichcov_kernelcov_matrix_estimatorcov_methodcov_selectcov_spectralprojgenerative_modelgenerative_processRbootrectangleslmtrapezetriangle
Readme and manuals
Help Manual
Help page | Topics |
---|---|
slm: A package for stationary linear models | slm-package |
Confidence intervals for the Model Parameters | confint.slm |
Covariance estimation by AR fitting | cov_AR |
Spectral density estimation: Efromovich method | cov_efromovich |
Kernel estimation: bootstrap method | cov_kernel |
Covariance matrix estimator for slm object | cov_matrix_estimator |
Methods to estimate the autocovariances of a process | cov_method |
Covariances Selection | cov_select |
Data-driven spectral density estimation | cov_spectralproj |
Some linear model | generative_model |
Some stationary processes | generative_process |
Plot.slm | plot.slm |
Predict for slm object | predict.slm |
Risk estimation for a tapered covariance matrix estimator via bootstrap method | Rboot |
Rectangular kernel | rectangle |
PM2.5 Data of Shanghai | shan |
Fitting Stationary Linear Models | slm |
slm class | slm-class slm.class |
Summarizing Stationary Linear Model Fits | summary.slm |
Trapeze kernel | trapeze |
Kernel triangle | triangle |
Calculate Variance-Covariance Matrix for a Fitted Model Object of class slm | vcov.slm |