Example coverage

Issue summary:

One of our goals has been to 1) make sure all examples run, and 2) look at example coverage. Implementing covr::package_coverage(type = c("examples")) is the function that allows us to do this. I'm happy to report that all examples appear to running on the roxygen2_documentation_v2 branch (yay!).

The report below details example coverage for future reference (and apologies for the output dump!). I'll look into GitHub workflows that can automate this in the future to try and give a nicer report. There is a GitHub action for this that I'm going to try to implement this afternoon: https://github.com/r-lib/actions/tree/master/examples#test-coverage-workflow (hopefully it will run examples?)

Overall hydromad package example coverage: 60.01%
R/cmd_unstable.R: 0.00%
R/eigenplot.R: 0.00%
R/evalRSM.R: 0.00%
R/eval_fdc.R: 0.00%
R/expuh3s.sim.R: 0.00%
R/fitByCMAES.R: 0.00%
R/fitByNsga2.R: 0.00%
R/fitByOptim1.R: 0.00%
R/fitDbmToPeaks.R: 0.00%
R/lambda.inverse.fit.R: 0.00%
R/lambda.inverse.sim.R: 0.00%
R/lambda.sim.R: 0.00%
R/ltv.R: 0.00%
R/maexpuh.R: 0.00%
R/prefilters.R: 0.00%
R/simhyd.R: 0.00%
R/tryModelOrders.R: 0.00%
R/xyplot.runlist.R: 0.00%
R/zzz.R: 0.00%
src/simhyd.cpp: 0.00%
R/expuh.fit.R: 5.19%
R/hydromad.stats.R: 16.61%
R/options.R: 20.93%
src/inversefilter.c: 31.88%
R/evalPars.R: 35.42%
R/evalParsRollapply.R: 42.11%
R/crossValidate.R: 44.64%
R/armax.inverse.fit.R: 44.97%
R/utilities.R: 46.38%
R/paretoObjectivesVaryWeights.R: 47.83%
R/cmd.R: 51.32%
R/buildTsObjective.R: 52.17%
src/cmd.c: 56.41%
R/armax.inverse.sim.R: 57.55%
R/tellTS.R: 60.38%
R/tfUtils.R: 60.45%
R/armax.sriv.fit.R: 60.75%
R/objFunVal.R: 61.62%
src/riv.c: 62.69%
R/estimateDelayFrac.R: 63.64%
R/fitByOptim.R: 63.89%
R/dbm.R: 64.29%
R/bucket.R: 66.67%
R/snow.R: 66.67%
R/expuh.sim.R: 67.09%
R/powuh.sim.R: 68.18%
R/runlist.R: 68.67%
R/hydromad.parallel.R: 69.23%
R/awbm.R: 72.46%
src/fland1.c: 74.11%
R/gr4j.R: 74.66%
R/armax.ls.fit.R: 75.00%
R/defineFeasibleSet.R: 75.76%
R/areModelsDominated.R: 76.47%
R/swimp.R: 76.47%
R/fitByDDS.R: 76.79%
R/simulate.hydromad.R: 77.55%
R/events.R: 77.78%
R/poweroid.R: 78.16%
R/filter_loss.R: 78.26%
R/hydromad.object.R: 78.83%
R/xyplot.hydromad.R: 78.89%
R/summary.hydromad.R: 79.46%
R/panel.ribbon.R: 80.77%
R/fitBySampling.R: 81.08%
R/nseStat.R: 81.36%
R/tf.R: 81.68%
R/cwi.R: 82.35%
R/runoffratio.R: 82.61%
R/predict.hydromad.R: 83.85%
R/sacramento.R: 83.87%
R/coef.hydromad.R: 84.31%
R/paretoCatchments.R: 84.35%
R/filter_tv.R: 85.71%
R/scalar.R: 85.71%
R/update.hydromad.R: 86.74%
R/paretoObjectivesNsga2.R: 86.84%
R/event.xyplot.hydromad.R: 86.96%
R/fitByDream.R: 87.80%
R/fitBySCE.R: 89.13%
R/estimateDelay.R: 89.47%
R/fitByDE.R: 90.24%
R/sce.R: 91.02%
R/routingfit.R: 91.43%
R/optimtrace.R: 93.18%
R/rotatedSampling.R: 93.75%
R/convertFlow.R: 93.85%
R/rollccf.R: 94.12%
R/nseVarTd.R: 94.44%
src/swimp.c: 95.45%
R/ParetoFiltering.R: 95.74%
R/event.xyplot.R: 96.88%
R/paretoTimeAnalysis.R: 97.06%
R/armax.sim.R: 97.78%
R/parameterSets.R: 98.28%
R/deconvolution.uh.R: 100.00%
R/findUnivariateBounds.R: 100.00%
R/hydromad.R: 100.00%
R/intensity.R: 100.00%
R/lagFrac.R: 100.00%
R/leakyExpStore.R: 100.00%
R/splitData.R: 100.00%
src/awbm.c: 100.00%
src/bucket.c: 100.00%
src/filters.c: 100.00%
src/gr4j.c: 100.00%
src/hydromad-init.c: 100.00%
src/sacramento.c: 100.00%
src/sacramento_state.c: 100.00%
src/snow.c: 100.00%