Thanks for the reply (:

Here is the result (I removed my email, and the probeFrame I created is under a different name "mapping_df", but those are the only changes).

> traceback()
11: stop(gettextf("arguments imply differing number of rows: %s", 
        paste(unique(nrows), collapse = ", ")), domain = NA)
10: data.frame(..., check.names = FALSE)
9: cbind(deparse.level, ...)
8: cbind(...)
7: eval(mc, env)
6: eval(mc, env)
5: eval(mc, env)
4: standardGeneric("cbind")
3: cbind(probeFrame, multiple)
2: makeChipDbFromDataFrame(probeFrame, orgPkgName, tax_id, genus, 
       species, dbFileName, optionalAccessionsFrame)
1: makeChipPackage(prefix = "SalmonChip", probeFrame = mapping_df, 
       orgPkgName = "org.Ssalar.eg.db", version = "1.0.0", maintainer = "Jaqueline <xxxx@mail.com>", 
       author = "Jaqueline <xxxx@mail.com>", outputDir = "R_Analysis", 
       tax_id = "8030", genus = "Salmo", species = "salar", optionalAccessionsFrame = NULL)

By looking at the code, after searching for makeChipPackage at https://code.bioconductor.org, under Code search. That will bring up this page, and you can see that the function is described in the first entry. If you click on the link it will bring up the .R file that contains the function plus some helper functions. This code was written by Marc Carlson, and his MO is to put the helper functions (things like .makeProbesTable that you can figure are internal helper functions because of the prepended dot) at the top, and the exported functions at the bottom. So scroll down to `makeChipPackage'.

You can see that makeChipPackage does some checking of the inputs at the beginning and then there's this part

 ## The file name for the DB
    dbFileName <- file.path(outputDir,paste0(prefix, ".sqlite"))
    ## Then make the DB
    makeChipDbFromDataFrame(probeFrame, orgPkgName, tax_id,
                            genus, species, dbFileName, optionalAccessionsFrame)

    ## choose the appropriate pkgTemplate (schema)
    orgSchema <- .getOrgSchema(orgPkgName)
    chipSchema <- .getChipSchema(orgSchema)

Which is about the only code that appears to do anything to the probeFrame. We need to find makeChipDbFromDataFrame so we scroll up a bit to find it, and eyeball that code. Now here is the trick; you won't find data.frame(..., check.names = FALSE) anywhere in that function, but it's there, right at the bottom of this code block:

 ## 1st connect to the org package
    require(orgPkgName, character.only = TRUE)
    ## Then make final column for the probeFrame            
    multiple <- unlist(lapply(as.character(probeFrame$probes),
                              .testForMultiples,
                              probes=as.character(probeFrame$probes)))
    probeFrame <- cbind(probeFrame, multiple)

Now here's why I say it's tricky. The cbind function is a special type of function called a 'method', which means that it behaves differently based on the input (in this case the input are data.frame). And there are more than one type of method in R, and they work differently depending on the type of method. Most of base R and the tidyverse use what's called S3 methods, and Bioconductor mostly uses S4 methods. It's complicated and boring and you don't need to know much about that, so for purposes of this discussion, we'll just say that cbind is both an S3 and an S4 method, and that an S3 method has the form function.object, so the version of cbind that is meant to operate on a data.frame is called cbind.data.frame. Additionally, not all methods are 'visible', meaning you can't just type the name of the function at an R prompt to see the code. But you can get a listing of all the methods for a function using the methods function:

> methods(cbind)
 [1] cbind,ANY-method                  cbind,Assays-method              
 [3] cbind,DataFrame-method            cbind,DataFrameList-method       
 [5] cbind,DelayedArray-method         cbind,FilterMatrix-method        
 [7] cbind,List-method                 cbind,NaArray-method             
 [9] cbind,RectangularData-method      cbind,Rle-method                 
[11] cbind,RleList-method              cbind,SparseArray-method         
[13] cbind,SparseArraySeed-method      cbind,SummarizedExperiment-method
[15] cbind,Tracks-method               cbind,VCF-method                 
[17] cbind.CompressedMatrix*           cbind.DGEList*                   
[19] cbind.DataFrame                   cbind.EList                      
[21] cbind.EListRaw                    cbind.List                       
[23] cbind.MAList                      cbind.RGList                     
[25] cbind.RectangularData             cbind.data.frame                 
[27] cbind.data.table*                 cbind.grouped_df*                
[29] cbind.gtable*                     cbind.integer64*                 
[31] cbind.ts*                        
see '?methods' for accessing help and source code

That's a lot of methods! You can see that there are three basic forms; cbind.object, cbind.object* (note the asterisk) and cbind,object-method. The first two are S3 methods, the difference being that the second S3 method is not exported, so you can't just type cbind.object to see the function, and the third one is an S4 method. Luckily for us, we already know we want cbind.data.frame, and there is no asterisk, so we can just type the function name to see what's in it.

> cbind.data.frame
function (..., deparse.level = 1) 
data.frame(..., check.names = FALSE)  <---------------- The important part is right here
<bytecode: 0x560a02552130>
<environment: namespace:base>

and you can then infer that this line of code

 probeFrame <- cbind(probeFrame, multiple)

will dispatch on cbind.data.frame, which calls data.frame(..., check.names = FALSE), which is what you see in your error, so we know that's the problem step. And having diagnosed where the error comes from, we can then look at the error itself, which is saying that you are trying to combine two data.frames, one of which has zero rows (arguments imply differing number of rows: 29093, 0). We already know that's not true for 'probeFrame', so it must be true for 'multiple'. And 'multiple' is generated just prior to the cbind call, using this code

 multiple <- unlist(lapply(as.character(probeFrame$probes),
                              .testForMultiples,
                              probes=as.character(probeFrame$probes)))

Which relies on the 'probeFrame' data.frame having a column called 'probes', and won't work if that name doesn't exist. As an example

> fakeo <- data.frame(probe.id = letters, gene.id = LETTERS)
> fakeo$probes
NULL

If the column names are any different, probeFrame$probes will return NULL. And you already said you called your columns 'probe.id' and 'gene.id', so I knew that was the problem.