Errors in running code in vignettes:
when running code in 'rsae.Rnw'

> library(rsae)

> data(landsat)

> names(landsat)
 [1] "SegmentsInCounty"   "SegementID"         "HACorn"            
 [4] "HASoybeans"         "PixelsCorn"         "PixelsSoybeans"    
 [7] "MeanPixelsCorn"     "MeanPixelsSoybeans" "outlier"           
[10] "CountyName"        

> table(landsat$CountyName)

Cerro Gordo    Hamilton       Worth    Humboldt    Franklin  Pocahontas 
          1           1           1           2           3           3 
  Winnebago      Wright     Webster     Hancock     Kossuth      Hardin 
          3           3           4           5           5           6 

> landsat[30:35, c("HACorn", "PixelsCorn", "PixelsSoybeans", 
+     "CountyName", "outlier")]
   HACorn PixelsCorn PixelsSoybeans CountyName outlier
30 122.66        342            182    Kossuth   FALSE
31 104.21        294            179    Kossuth   FALSE
32  88.59        220            262     Hardin   FALSE
33  88.59        340             87     Hardin    TRUE
34 165.35        355            160     Hardin   FALSE
35 104.00        261            221     Hardin   FALSE

> linmodel <- lm(HACorn ~ PixelsCorn + PixelsSoybeans, 
+     data = landsat)

> plot(linmodel, 5)

> bhfmodel <- saemodel(formula = HACorn ~ PixelsCorn + 
+     PixelsSoybeans, area = ~CountyName, data = subset(landsat, 
+     subset = (outlier == F .... [TRUNCATED] 

> bhfmodel
SAE MODEL TYPE: B (J.N.K. Rao's classification)
--- 
FIXED EFFECTS: HACorn ~ (Intercept) + PixelsCorn + PixelsSoybeans 
AREA-SPECIFIC RANDOM EFFECTS: CountyName 

> summary(bhfmodel)
Model summary: 
saemodel(formula = HACorn ~ PixelsCorn + PixelsSoybeans, area = ~CountyName, 
    data = subset(landsat, subset = (outlier == FALSE)))
---
No. of areas:  12 
No. of obs.:  36 
Smallest area:  1  units 
Largest area:  5  units 

> mlfit <- fitsaemodel("ml", bhfmodel)

> mlfit
ESTIMATES OF SAE-MODEL (model type B) 
Method:  Maximum likelihood estimation 
---
Fixed effects
Model: HACorn ~ (Intercept) + PixelsCorn + PixelsSoybeans 
  Coefficients: 
   (Intercept)      PixelsCorn  PixelsSoybeans  
     50.967557        0.328581       -0.133710  
--- 
Random effects 
  Model: ~1| CountyName 
           (Intercept)  Residual
Std. Dev.  11.0028      11.7181 
--- 
Number of Observations:  36 
Number of Areas:  12 


> summary(mlfit)
ESTIMATION SUMMARY 
Method:  Maximum likelihood estimation 
---
Fixed effects
                    Value  Std.Error    t-value df    p-value    
(Intercept)    50.9675570 23.4750890  2.1711337 22   0.040984 *  
PixelsCorn      0.3285805  0.0479840  6.8477166 22 7.0475e-07 ***
PixelsSoybeans -0.1337099  0.0530628 -2.5198431 22   0.019502 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> failconvg <- fitsaemodel("ml", bhfmodel, niter = 1)

> failconvg
THE METHOD DID NOT CONVERGE!
---
  1) use convergence() of your fitted model to learn more 
  2) study the documentation using the command ?fitsaemodel 
  3) you may call fitsaemodel with 'init' equal to (either) 'lts'
     or 's' (this works also for ML, though it may no be very efficient)
  4) if it still does not converge, the last resort is to modify
     'acc' and/or 'niter' (and hope and pray)

> convergence(failconvg)
CONVERGENCE REPORT 
NOTE: ALGORITHM DID NOT CONVERGE!
---
User specified number of iterations (niter) and 
numeric precision (acc):

                 niter  acc  
overall loop     1e+00  1e-05
fixeff           2e+02  1e-05
residual var     2e+02  1e-05
area raneff var  1e+02  1e-05
---
Number of runned EE-specific iterations in each 
call (given the user-defined specs), reported for 
each of the  1 overall iterations separately: 

   fixeff  residual var  area raneff var
1   2       2            18             

> set.seed(12345)

> n <- 200

> beta <- c(1, 1)

> cst <- rep(1, n)

> x <- rnorm(n)

> y <- as.matrix(cbind(cst, x)) %*% beta + rnorm(n)

> areaid <- rep(1:10, each = 10)

> df <- data.frame(y = y, x = x, areaid = areaid)

> lm(y ~ x, data = df)

Call:
lm(formula = y ~ x, data = df)

Coefficients:
(Intercept)            x  
      1.080        1.034  


> fakemlm <- saemodel(y ~ x, area = ~areaid, data = df)

> fitsaemodel("ml", fakemlm)
ESTIMATES OF SAE-MODEL (model type B) 
Method:  Maximum likelihood estimation 
---
Fixed effects
Model: y ~ (Intercept) + x 
  Coefficients: 
(Intercept)            x  
    1.07965      1.03365  
--- 
Random effects 
  Model: ~1| areaid 
---
NOTE THAT THE VARIANCE OF THE AREA-LEVEL RANDOM 
EFFECT IS ALMOST ZERO! DO YOU REALLY NEED THE 
RANDOM EFFECT? IF SO, GO AHEAD. HOWEVER, YOU 
SHOULD CONSIDER FITTING A (ROBUST) GLS MODEL.
---
           (Intercept)  Residual
Std. Dev.  0.000000     0.955232
--- 
Number of Observations:  200 
Number of Areas:  10 


> huberfit <- fitsaemodel("huberm", bhfmodel, k = 1.5)

> huberfit
ESTIMATES OF SAE-MODEL (model type B) 
Method:  Huber-type M-estimation 
Robustness tuning constant: k =  1.5 
---
Fixed effects
Model: HACorn ~ (Intercept) + PixelsCorn + PixelsSoybeans 
  Coefficients: 
   (Intercept)      PixelsCorn  PixelsSoybeans  
     50.284889        0.328453       -0.139159  
--- 
Random effects 
  Model: ~1| CountyName 
           (Intercept)  Residual
Std. Dev.  11.9537      12.3639 
--- 
Number of Observations:  36 
Number of Areas:  12 


> summary(huberfit)
ESTIMATION SUMMARY 
Method:  Huber-type M-estimation 
Robustness tuning constant:  k  =  1.5 
---
Fixed effects
                    Value  Std.Error    t-value df    p-value    
(Intercept)    50.2848895 24.8465096  2.0238211 22   0.055301 .  
PixelsCorn      0.3284534  0.0507730  6.4690607 22 1.6536e-06 ***
PixelsSoybeans -0.1391587  0.0561777 -2.4771135 22   0.021409 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Degree of downweighting/winsorization:

                 sum(wgt)/n
fixeff           0.984130  
residual var     0.972323  
area raneff var  0.984131  

> convergence(huberfit)
CONVERGENCE REPORT 
---
User specified number of iterations (niter) and 
numeric precision (acc):

                 niter  acc  
overall loop     4e+01  1e-05
fixeff           2e+02  1e-05
residual var     2e+02  1e-05
area raneff var  1e+02  1e-05
---
Number of runned EE-specific iterations in each 
call (given the user-defined specs), reported for 
each of the  11 overall iterations separately: 

    fixeff  residual var  area raneff var
1    3      10            19             
2    9      10            13             
3   11       7            12             
4    4       7            12             
5    8       6            12             
6    6       5            12             
7    5       4            12             
8    4       3            12             
9    3       3            12             
10   2       2            12             
11   1       1            12             

> fitsaemodel("huberm", bhfmodel, k = 1.2, init = "s")
Loading required package: robustbase
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called 'robustbase'

  When sourcing 'rsae.R':
Error: You cannot use 'lts' or 's', because the 
 'robustbase' package is not installed! 
Execution halted

  'rsae.Rnw' using 'UTF-8'... failed