lci | A lambda calculus interpeter

I expect identical results calculating a CI for a proportion using qbinom and binom.test, but they are actually slightly different:

I am trying to figure out how to autofill a specific range of cells based on already defined data in the same row. My task is more complex, but a simplification of the step can be seen in the code below. What I want to achieve is:

1. Define a range where I want to output my values
2. Multiply two values in the same row of the selected range cell (to its left), and output this number in the currently selected range cell. To do this, one of the numbers to be multiplied will be dependant on a string also in the row (which describes its type).
3. Loop through the defined range and repeat the calculation on each row.

My current code is as follows and outputs a "application defined or object defined error".

Any help would be much appreciated.

I have imported ecoinvent 3.7.1 on a brightway project and i followed a few tutorials to understand brightway set up procedures and usage.

I want to use brightway to perform calculations on the inventory data of some processes (in particular i want to sum all emissions to air of CO2, CH4, N2O.

I tried with:

I want to analyse individual data frames within a list of data frames and then store the results of each individual analysis in a new data frame. Any advice would be great, i've tried lapply() and a for loop but can't get either working.

I have a list of data frames:

I'm trying to receive stock data for about 1000 stocks, to speed up the process I'm using multiprocessing, unfortunately due to the large amount of stock data I'm trying to receive python as a whole just crashes.

Is there a way to use multiprocessing without python crashing, I understand it would still take some time to do all of the 1000 stocks, but all I need is to do this process as fast as possible.

I'm having the hardest time generating confidence intervals for my glmer poisson model. After following several very helpful tutorials (such as https://drewtyre.rbind.io/classes/nres803/week_12/lab_12/) as well as stackoverflow posts, I keep getting very strange results, i.e. the upper and lower limits of the CI are identical.

Here is a reproducible example containing a response variable called "production," a fixed effect called "Treatment_Num" and a random effect called "Genotype":

I am plotting a smooth to my data using geom_smooth and using geom_ribbon to plot shaded confidence intervals for this smooth. No matter what I try I cannot get a single legend that represents both the smooth and the ribbon correctly, i.e I am wanting a single legend that has the correct colours and labels for both the smooth and the ribbon. I have tried using + guides(fill = FALSE), guides(colour = FALSE), I also read that giving both colour and fill the same label inside labs() should produce a single unified legend.

Any help would be much appreciated.

Note that I have also tried to reset the legend labels and colours using scale_colour_manual()

The below code produces the below figure. Note that there are two curves here that are essentially overlapping. The relabelling and setting couours has worked for the geom_smooth legend but not the geom_ribbon legend and I still have two legends showing which is not what I want.

I've tried to write an implementation of the Weasel Program. I have compiled three versions, all exactly identical, with the names "weasel.exe", "weasel2.exe", and "weasel3.exe". "weasel.exe" produces the expected output,

I have a dataframe, df :