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cleanup docs after tidying repo
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DerivsNLS.Rmd

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@@ -428,7 +428,7 @@ continuing effort to bring them into other R tools.
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The following example shows that numericDeriv() does a reasonable job of computing
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the Jacobian, but the result is still singular.
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```{r code=xfun::read_utf8('badJlogmod.R')}
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```{r code=xfun::read_utf8('Tests/Examples/badJlogmod.R')}
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```
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## From nls.R
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```{r nlsR, comment=NA, echo=FALSE}
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cat(readLines('./nls-numericDeriv.R'), sep = '\n')
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cat(readLines('archived/nls-numericDeriv.R'), sep = '\n')
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```
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## From nls.c
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```{r nlsc, comment=NA, echo=FALSE}
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cat(readLines('./nls-numericDeriv.c'), sep = '\n')
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cat(readLines('archived/nls-numericDeriv.c'), sep = '\n')
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```
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# Appendix 2: numericDeriv() from nlsalt package (all in R)

DerivsNLS.pdf

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MachineSummary.pdf

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PkgFromRbase.pdf

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VarietyInNonlinearLeastSquaresCodes.Rmd

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??FIXME??
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```{r hobbtime1}
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source("HobbsTiming.R", echo=TRUE)
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source("Tests/Examples/HobbsTiming.R", echo=TRUE)
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```
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WorkingDocument4ImproveNLS.Rmd

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* In reverse date order *
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[2021-07-25 - 2021-07-26]
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- AB: Got a working workaround function to resolve RAM
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issue for Windows (probably; Machine ID is now machine dependent. Need to
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make it robust??). Started "runner_v2" to compare various NLS methods.
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Observed that "plinear" created an error `"Error in nls(formula =
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NLSformula, data = NLSdata, start = NLSstart, algorithm =
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NLSmethods$algorithm[j]) :
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step factor 0.000488281 reduced below 'minFactor' of 0.000976562"`.
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There is an issue with "control" in "methods.csv" file. Trying to resolve it.
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Spreadsheet is running successfully, but with default options only. Should
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column names be extended??
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### [2021-7-25]
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- JN: continued work on nlsj to check features working. Some rewrite of sections

WorkingDocument4ImproveNLS.pdf

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