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data {
  int<lower=0> N;
  int<lower=0> D;
  matrix[N, D] x;
  array[N] int<lower=0, upper=1> y;

  real<lower=0> h;
  int<lower=0> B;
  array[B] vector[D] beta0;

soodoku

#!/usr/bin/env python3
import argparse
import json
import requests
import sys
from urllib.parse import quote

def parse_arguments():
    """Parse command line arguments"""
    parser = argparse.ArgumentParser(description="List Dataverse datasets owned by a user and convert to JSON")

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To go with Andrew's https://statmodeling.stat.columbia.edu/2017/05/19/continuous-hinge-function-bayesian-modeling/

Also includes alternate smoothers outside of logistic.

Used Claude (but verified).

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#!/usr/bin/env python3
"""
Dataverse File Uploader

A script to upload files to Harvard Dataverse or other Dataverse instances.
"""

import os
import argparse
import requests

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soodoku

Scraping the abstracts for the discussion papers by i4replication.org.

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RQ Boot to Latex

ModelSummary and Stargazer fail when RQ s.e. calculation fails

stargazer(m_demo_int_qr)
Error in base::backsolve(r, x, k = k, upper.tri = upper.tri, transpose = transpose,  : 
  singular matrix in 'backsolve'. First zero in diagonal [13]

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-------------

set.seed(123)

# Step 1: Simulate Population
num_strata <- 10  # Number of strata
population_size <- 100000  # Total population size

# Generate strata sizes and proportions

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# Set random seed for reproducibility
set.seed(123)

# Time t parameters
n_novels_x_t <- 500    # 50% of 1000 novels are type x at time t
n_novels_y_t <- 500    
k <- 30               # 30% more likely to read type x
total_readings <- 800 # Fixed total reading capacity

# Time t simulation