Aggregating or Pooling Multinomial Logistic Regression Coefficients

I'm trying to recreate the MNL analysis like this paper or this one.

In Excel I have a sheet with my choice column which contains the market share on each itinerary. Alternatively I also have the number of passengers on that itinerary that could be used instead as the choice variable. I then have several variables that describe the utility of the given itinerary that are similar to the ones in the papers above as my independent variables.

The problem I'm having is that rather than spiting out the coefficients for my independent variables for the entire dataset like shown on page 22 of the first paper or page 18 of the second paper, I'm getting the variables for each choice variable in a huge list.

Currently I'm trying to use the mlogit package in R. I have also tried XLStats with similar challenges. Accord.NET seems to produce the output I like but it appears to be doing some kind of binomial logistic regression and not MNL. It is also not as robust of package.

simplified data example
Response variable: Market Share

Origin/Dest/Carrier (nor used in model included here for context)
Predictor 2: IsNonstop?(binary variable)
Predictor 3: NumberOfSeats

50% DEN/JFK/AA 1 5000
20% DEN/JFK/UA 0 3000
30% DEN/JFK/DL 0 8000
25% DFWLAX/F9 0 10000
75% DFW/LAX/WN 1 25000

desired output format
IsNonstop? 2.30
NumberOfSeats .12

actual mlogit output format
50% IsNonstop 1.9 NumberOfSeats .45
20% IsNonstop 1.4 NumberOfSeats .015
30% IsNonstop -41.9 NumberOfSeats .85

I just wanted to share with everyone my solution in the event others run into a similar issue. The answer was that the log likelihood formula in the papers were a bit beyond what could be simply done in some of the common programs that I mentioned. My answer was to implement the log likelihood function from the Grosche paper in Excel using solver with only a few attributes and records. Now that I got it working I'm going to go implement it in a more robust solution.