That “gradual decline” is an artifact of your maths, in which you're gradually changing the weight of recent years.
Consider a sequence with an extreme drop-off: 100, 100, 100, 100, 40. Taking averages of all the numbers, then all but the first, all but the first 2, and so on, yields: 88, 85, 80, 70, 40. That might look like it includes a gradual decline, but clearly there's nothing gradual in the underlying data.
Thanks. I'd previously tried and failed a couple of times to get a NeoVim gui installed, mainly because I wanted to try one of those ligature fonts that display -> as → and the like. There seemed to be a few different options, in various states of being abandoned, or unpackaged, or needing libraries not in my OS, or ...
Turns out now all that's needed for Ubuntu is sudo apt install neovim-qt. Thank you for prompting me to look at this again.
However, on running it, the window seemed very wide, so one of the first things I tried was `:set columns=80` — and nothing happened. Checking :h 'co`, it looks like it's supposed to work.
I avoid that by replying inline, discarding the history of multiple-quoted emails from top-repliers. Everybody else uses Outlook, but nobody has yet complained.
For sending emails with formatting (italics, headings), I use Markdown in Vim then press H in Mutt before sending, which pipes it through an appropriate filter:
set send_multipart_alternative_filter=html_alternative
send-hook . 'set send_multipart_alternative=no'
macro compose H ':set send_multipart_alternative=yes<Enter><view-alt-mailcap>' 'add HTML alternative'
One of my main reasons for using Mutt is precisely the opposite: with mbsync I have copies of all emails on my laptop, so I can read, search, and compose emails when I don't have an internet connection.
Consider a sequence with an extreme drop-off: 100, 100, 100, 100, 40. Taking averages of all the numbers, then all but the first, all but the first 2, and so on, yields: 88, 85, 80, 70, 40. That might look like it includes a gradual decline, but clearly there's nothing gradual in the underlying data.