Description slm function, in "slm-main.R" Methods for slm, in "slm-method.R" Others functions, in "auxiliary-fun.R" Generative functions, in "generative.R" Data References

The `slm`

package enables to fit linear models on datasets considering the dependence between the observations.
Most of the functions are based on the functions and methods of `lm`

, with the same arguments and the same format for the outputs.

`slm`

function, in "slm-main.R"The `slm`

function is the main function of this package. Its architecture is the same as the `lm`

function
but it takes into account the possible correlation between the observations. To estimate the asymptotic covariance matrix of
the least squares estimator, several approaches are available: "fitAR" calls the
`cov_AR`

function, "spectralproj" the `cov_spectralproj`

function, "kernel" the `cov_kernel`

function,
"efromovich" the `cov_efromovich`

function and "select" the `cov_select`

function. The "hac" method uses the `sandwich`

package,
and more precisely, the method described by Andrews (1991) and Zeileis (2004).

`slm`

, in "slm-method.R"The `slm`

function has several associated methods, which are the same as for the `lm`

function.
The available methods are: `summary`

, `confint`

, `predict`

, `plot`

and `vcov`

.

The package has some auxiliary functions, in particular some predefined kernels for the kernel method of `slm`

function: the
trapeze kernel, the triangle kernel and the rectangular kernel. The user can also define his own kernel and put it in the argument
`kernel_fonc`

in the `slm`

function.

The `generative_process`

function generates some stationary processes.
The `generative_model`

function generates some designs.

The package contains a dataset "shan". This dataset comes from a study about fine particle pollution in the city of Shanghai. The data are available on the following website https://archive.ics.uci.edu/ml/datasets/PM2.5+Data+of+Five+Chinese+Cities#.

D. Andrews (1991). Heteroskedasticity and autocorrelation consistent covariant matrix estimation. *Econometrica, 59(3), 817-858*.

E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. *arXiv preprint arXiv:1906.06583*.
https://arxiv.org/abs/1906.06583.

A. Zeileis (2004). Econometric computing with HC and HAC covariance matrix estimators.

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