semvec/pypi-packages huggingface dataset:
SurvivalScale 0.0.10
Package to compute survival scale estimates.
Requires Python: >=3.9
Keywords: Bootstrap, Statistics, Survey

# Estimate Likert-based Items with Survival Scale

This estimator is used with survey data measured using Likert scales as the dependent variable. It envisions the Likert scale as a series of trials where each trial has a probability of success characterized by a set of features on a logit scale. This approach allows the bottom and top box of the Likert scale to have different probabilities of success (censoring) than the middle boxes.  This model is capturing the idea that very unhappy and very happy respondents represent a much more extreme position than those in the middle of the scale.

This estimator is a expansion of the estimators described in the following papers:

Smith, B. O., & Wooten, J. J. (2023). Assessing proxies of knowledge and difficulty with rubric-based instruments. Southern Economic Journal, 90(2), 510–534. https://doi.org/10.1002/soej.12658

Smith, B. O., & Wooten, J. J., (2024 - Working Paper). Are Students Sexist when Rating Each Other? Bias in Peer Ratings and a Generalization of the Rubric-Based Estimator. Available at SSRN: https://dx.doi.org/10.2139/ssrn.4858815

These papers focus on the use of this estimator in educational settings.  This paper and software allow the bottom box to have different probabilities of success than the middle boxes thus making it more suitable for Likert scale data.  This estimator and an application is described in a upcoming paper:

Smith, B. O., Klucarova, S., & Wooten, J. J., (2025 - Working Paper) What Advertising Techniques Increase Stated Preference for Microtransit: An Application of a New Estimation Technique. Available on SSRN: https://papers.ssrn.com/abstract=5759902

This new estimator is not a replacement for ordered logit or probit models.  It is a different way of looking at the data that may be more suitable in some settings. 

## Example
```python
from SurvivalScale import get_results
# data is a pandas DataFrame containing your survey data
cj, beta, metrics = get_results(data, bootstrap_iterations=1000, alpha=0.05, columns=['feature1', 'feature2'], block_id='block')
```

## Installation

You can install the package from either PyPI or the Conda repository:

```console
pip install SurvivalScale
```
or

```console
conda install -c tazzben survivalscale
```
avahi-json 1.1.0
Output details of hosts on the network found by Linux's avahi mdns server in JSON. 

# avahi-json
**@readwithai** - [X](https://x.com/readwithai) - [blog](https://readwithai.substack.com/) - [machine-aided reading](https://www.reddit.com/r/machineAidedReading/) - [📖](https://readwithai.substack.com/p/what-is-reading-broadly-defined
)[⚡️](https://readwithai.substack.com/s/technical-miscellany)[🖋️](https://readwithai.substack.com/p/note-taking-with-obsidian-much-of)

Query the local network for devices and services using MDNS bia avahi (like `avahi-browser -ar`) but output results in easy-to-use JSON.

## Motivation
I have an Android phone on my home network. I also use KDE connect on the phone. I occassionally want to connect to this phone via ADB over a TCP connection, but the IP address is not fixed.

I could use KDE connect to find the device since KDE connect advertises itself using an MDNS services (i.e. avahi). This would be all well and good - but the avahi command-line tool neither allows me to directly query for my phone, nor does it produce machine-readable output.

Therefore I am writing something which queries for services on the network using avahi and outputs theresult in beautiful JSON. I am slightly surprised that no one has done this before.

## Installation
You must first install the `python3-avahi` bindings. These are handled by `apt` on Ubuntu and can be installed with `sudo apt install python3-avahi`. 

You can then install `avahi-json` itself using [pipx](https://github.com/pypa/pipx) with the following command (`--system-site-packages` is needed for avahi and dbus):

```
pipx install --system-site-packages avahi-json
```

You need to have avahi, Linux's mdns daemon, installed to use this. But this tends to be installed by default.

## Usage
Run `avahi-json`: this will give you services on the network with a separate JSON entry on each line.

## About me
I am **@readwithai**. I create tools for reading, research and agency sometimes using the markdown editor [Obsidian](https://readwithai.substack.com/p/what-exactly-is-obsidian).

I also create a [stream of tools](https://readwithai.substack.com/p/my-productivity-tools) that are related to carrying out my work.

I write about lots of things - including tools like this - on [X](https://x.com/readwithai).
My [blog](https://readwithai.substack.com/) is more about reading and research and agency.
brewing 0.0.12rc3
A web framework built on sqlachemy and fastapi.
Requires Python: >=3.14

# brewing

[![CI](https://github.com/mikemcowie/brewing/actions/workflows/ci.yaml/badge.svg)](https://github.com/mikemcowie/brewing/actions/workflows/ci.yaml)

Brewing is a python application framework designed to solve problems well, and then get out of your way.



## Installation

Use your preferred python package manager to install brewing. The author strongly recommends [uv](https://docs.astral.sh/uv/).

```
uv add brewing
```

# Influences

Brewing attempts to take principals from various battle-tested frameworks.

## Rails

1.The [rails doctrine](https://rubyonrails.org/doctrine), especially:
   * *convention over configuration*
   * *the menu is omakase*
   * *provide sharp knives*

## Django

* The [*batteries included* ](https://docs.python.org/3/tutorial/stdlib.html#tut-batteries-included) principal
* Basically, following much of the rails doctrine in a python context.
* Class based views with close integration to the model/data layer.

## FastAPI

* Using type hints at runtime to setup HTTP endpoints.
* Brewing's ASGI application is a subclass of fastapi.FastAPI, and its key decorators are largely maintained.