In order to demonstrate `SCtools`

it is useful to start
with a replication of the cannonical Basque study from Abadie and
Gardeazabal (2003).

```
library(SCtools)
library(Synth)
```

Now we can load the `basque`

data set.

`data("basque")`

As per the normal `Synth`

workflow, we need to reformat
our data using the `dataprep`

function in which we specify
our counterfactuals and our response variables.

```
<- dataprep(
dataprep.out foo = basque,
predictors = c("school.illit", "school.prim", "school.med",
"school.high", "school.post.high", "invest"),
predictors.op = "mean",
time.predictors.prior = 1964:1969,
special.predictors = list(
list("gdpcap", 1960:1969 ,"mean"),
list("sec.agriculture", seq(1961, 1969, 2), "mean"),
list("sec.energy", seq(1961, 1969, 2), "mean"),
list("sec.industry", seq(1961, 1969, 2), "mean"),
list("sec.construction", seq(1961, 1969, 2), "mean"),
list("sec.services.venta", seq(1961, 1969, 2), "mean"),
list("sec.services.nonventa", seq(1961, 1969, 2), "mean"),
list("popdens", 1969, "mean")),
dependent = "gdpcap",
unit.variable = "regionno",
unit.names.variable = "regionname",
time.variable = "year",
treatment.identifier = 17,
controls.identifier = c(2:16, 18),
time.optimize.ssr = 1960:1969,
time.plot = 1955:1997)
```

Now, we can run the SCM algorithm using the `synth`

function.

`<- synth(data.prep.obj = dataprep.out, method = "BFGS") synth.out `

`Synth`

provides some additional helper functions to
extract information from the outputted object including the ability to
analyze the outputs:

`<- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w) gaps `

And generate plots of the counterfactual:

```
path.plot(synth.res = synth.out, dataprep.res = dataprep.out,
Ylab = "real per-capita GDP (1986 USD, thousand)", Xlab = "year",
Ylim = c(0, 12), Legend = c("Basque country",
"synthetic Basque country"),
Legend.position = "bottomright")
```

At this point, `SCtools`

extends the analysis from
`Synth`

, While `Synth`

generates an analysis on
one configured dataset, `SCtools`

provides the tooling to
permute the dataset and generate multiple placebos to test the
sensitivity of our SCM output.

```
<- generate.placebos(dataprep.out = dataprep.out,
placebo synth.out = synth.out, strategy = "multiprocess")
```

We can then use the `plot_placebos`

to run a placebo test
for the findings in Abadie and Gardeazabal (2003).

`plot_placebos(placebo)`

Finally, we can also run the `mspe_plot`

function to run a
post/pre MPSE test for that case, and find how unlikely it would be to
find by chance the effects identified.

`mspe_plot(placebo)`