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Diagnostic plots for fitted regression model, i.e., objects of class

`svyreg_rob`

Robust regression: If the estimated regression scale (by default weighted MAD) is zero (or nearly so), the weighted IQR is tried instead. If the weighted IQR ist also zero, the function returns with an error.

Function

`mer()`

for minimum estimated risk estimation of location gained two new arguments:`method`

: the method used in the search for a minimum, e.g.,`"Brent"`

,`"BFGS"`

, see`stats::optim()`

for more details`init`

determines the left side of the search interval and the initial value in the minimization approach

Function

`mse()`

computes/ extracts the estimated mean square error/ estimated risk in presence of representative outliers; see also`mer()`

Robust generalized regression estimation (GREG) of the mean and total; see

`svymean_reg()`

and`svytotal_reg()`

. The current implementation of the functions is**EXPERIMENTAL**and a warning is issued when calling the functions (unless`verbose = FALSE`

). Experimental features may:- have undergone less extensive testing than is normal for standard features
- interact with unstable (external) dependencies
- be subject to change
- not be directly supported by the developers in the event issues arise

- The default functions for regression M-estimators are now called
`svyreg_huberM()`

and`svyreg_tukeyM()`

; the old functions`svyreg_huber()`

and`svyreg_tukey()`

are deprecated but are kept for compatibility reasons. - Documentation files in folder
`inst/doc`

and test cases in`tests`

have been updated - Several internal changes (e.g., default value of
`k_Inf`

is now`1e06`

not`1e05`

; see function`svyreg_control()`

).

For designs with unequal probability sampling, the variance estimates
of the robust estimators of mean and total are now identical with the
estimates of `survey::svymean()`

and
`survey::svytotal()`

if the tuning constant is
`k = Inf`

or `LB = 0`

and `UB = 1`

Added `DOI`

to all references (where available).

- Weighted regression GM-estimators; see e.g.,
`svyreg_huberGM()`

- M- and GM-estimator of regression with Tukey biweight psi-function;
see
`svyreg_tukey()`

and`svyreg_tukeyGM()`

- Weighted
`k`

winsorized mean and total; see`weighted_mean_k_winsorized()`

and`svymean_k_winsorized()`

- Dalen’s weight reduction estimator of the mean and total; see
`weighted_mean_dalen()`

and`svymean_dalen()`

- Weighted M-estimator of the mean and total with Tukey biweight psi-function
- Weighted Huber Proposal 2 estimator; see
`huber2()`

- Data sets
`counties`

,`flour`

,`losdata`

, and`MU284strat`

The original C implementation of `wquantile`

was buggy
(with implications for the R function `weighted_quantile()`

and also the iterative re-weighted least squares algorithm). The new C
implementation of `wquantile`

is sound.

Argument `type = "rwm"`

of
`weighted_mean_huber()`

is not used anymore (deprecated);
instead, the type is now called `"rhj"`

.

- The authors agreed on November 25, 2020, to license/ re-license the package version 0.2 under the GPL-2 resp. GPL-3 license. (Version 0.1 has been licensed under the MIT license).
- Tobias Schoch is now the maintainer of the package.