modelsummary | Model summary in PyTorch | Machine Learning library

My current dataset looks like:

I am trying to run a loop which takes different columns of a dataset as the dependent variable and remaining variables as the independent variables and run the lm command. Here's my code

This question is similar in spirit to Modelsummary: Different formats for estimates and statistics, but concerns the formatting for "additional rows" created with the add_rows function of the (excellent) modelsummary package. As far as I can tell, these cannot be formatted in a similar manner (though hopefully I'm not missing something basic!). Here's a simple reproducible example.

I'm trying to produce tables in R (not RMarkdown, as this question helps answer) with modelsummary and I'm having a tough time adding reference labels (e.g., tab:hello). As Vincent points out in this answer, extra arguments to modelsummary should be pushed forward automatically, but it seems this does not work with LaTeX labels. Here's an example:

So I am running multiple cochrane orcutt regressions, which is no problem. I then display the output of these regressions using modelsummary(). Still no problems up to this point.

However, when I then try to compare the models using modelplot(), there are no confidence intervals computed in the cochrane orcutt model (class "orcutt") and I thus get the following error:

Error in eval(parse(text = text, keep.source = FALSE), envir) : object 'conf.low' not found

I know what the problem here is - there are just no confidence interval "parts" computed by the cochrane.orcutt() command. A partial solution is also obvious - I can just calculate the confidence intervals using the point estimates/coefficients and the standard errors (which are of course included in the model by default).

However my problem arises when I want to use these confidence interval values in modelplot(), because they are not "in" the model object. In my ignorance, I attempted the following to try and create the lower bound of a confidence interval, using mutate():

apologies if I write anything wrong - this is my first post. I am doing some time series regressions on the different determinants of house prices for different countries, and would like to use modelplot() to showcase the different coefficients for one variable across all the countries. This is the easy part, and I have it done. However, when I try to run this code to try and include Newey-West std. errors:

I am trying to create a table with descriptive statistics for the whole sample as well as subgroups. My goal is to use the wonderful modelsummary R package to return one table with mean, sd, min, median, max, and graphs for the variables calculated for the entire sample as well as mean and sd for every group. I was able to achieve this with two separate tables. However, I would like to have all this information in a single table with the statistics about the entire sample first (see Fig. 1) and the subgroups second (see Fig. 2). If possible, I would also like to add the first-level heading for the whole sample and name it "All" or "Entire sample." Lastly, given that journals in my field require the use of the APA style, I wonder if the table can be turned into this format (e.g., with all the required borders, text in black instead of grey, etc.) (see Fig. 3). If modelsummary does not handle this, I am also open to trying other packages. Thanks so much to anyone who will help!

I am using gamm4:gamm4 to model longitudinal change.

I am trying to use the modelsummary::modelsummary function to create an output table of the following results:

I would like to add t-values and std.error to the output of the fixed effects, and remove the empty tags values from the random effects

I JUST found out about this amazing R package, modelsummary.

It doesn't seem like it offers an ability to transpose regression outputs.

I know that you cannot do a tranposition within kable-extra, which is my go-to for ordinary table outputs in R. Since modelsummary relies on kable-extra for post-processing, I'm wondering if this is possible. Has anyone else figured it out?

Ideally I'd like to preserve the stars of my regression output. This is available in STATA (below):

Thanks in advance!