lead_standalone.stan

data {
	int<lower=0> numLscores; // Number of boulder results
	int<lower=0> maxN; // Max climber number observed
	int<lower=0> maxML; // Max lead comp number observed
	
	vector[numLscores] lscore;
	int<lower=1,upper=maxN> lClimber[numLscores];
	int<lower=1,upper=maxML> lComp[numLscores];
	
	real hypersigma_LScore;
}
parameters {
	vector[maxN] ls; // Latent lead skill
  	vector[maxML] al0; // Lead score intercept, per comp
	vector[maxML] al1; // lead skill->score slope, per comp
	
	vector<lower=0>[maxML] sigmaL; // Noise in bt, per comp
}
model {
	// Model bouldering scores via bouldering skill latents
	for (i in 1:numLscores) {
		int comp = lComp[i];
		int climber = lClimber[i];
		lscore[i] ~ normal(al0[comp] + al1[comp] * ls[climber], sigmaL[comp]);
	}
	
	ls ~ normal(0, 1);
	sigmaL ~ exponential(hypersigma_LScore);
	al1 ~ normal(1, 1);
}