Removing stan files from root directory since they are now included in both R and Python directory. This means code duplication, but it seems that there can be issues in Windows with symlinks in github (http://stackoverflow.com/questions/5917249/git-symlinks-in-windows) so we are better off not using them.

stan/prophet_linear_growth.stan

@@ -1,40 +0,0 @@
-data {
-  int T;                                // Sample size
-  int<lower=1> K;                       // Number of seasonal vectors
-  vector[T] t;                            // Day
-  vector[T] y;                            // Time-series
-  int S;                                // Number of split points
-  matrix[T, S] A;                   // Split indicators
-  int s_indx[S];                 // Index of split points
-  matrix[T,K] X;                // season vectors
-  real<lower=0> sigma;              // scale on seasonality prior
-  real<lower=0> tau;                  // scale on changepoints prior
-}
-
-parameters {
-  real k;                            // Base growth rate
-  real m;                            // offset
-  vector[S] delta;                       // Rate adjustments
-  real<lower=0> sigma_obs;               // Observation noise (incl. seasonal variation)
-  vector[K] beta;                    // seasonal vector
-}
-
-transformed parameters {
-  vector[S] gamma;                  // adjusted offsets, for piecewise continuity
-
-  for (i in 1:S) {
-    gamma[i] = -t[s_indx[i]] * delta[i];
-  }
-}
-
-model {
-  //priors
-  k ~ normal(0, 5);
-  m ~ normal(0, 5);
-  delta ~ double_exponential(0, tau);
-  sigma_obs ~ normal(0, 0.5);
-  beta ~ normal(0, sigma);
-
-  // Likelihood
-  y ~ normal((k + A * delta) .* t + (m + A * gamma) + X * beta, sigma_obs);
-}

stan/prophet_logistic_growth.stan

@@ -1,63 +0,0 @@
-data {
-  int T;                                // Sample size
-  int<lower=1> K;                       // Number of seasonal vectors
-  vector[T] t;                            // Day
-  vector[T] cap;                          // Capacities
-  vector[T] y;                            // Time-series
-  int S;                                // Number of split points
-  matrix[T, S] A;                   // Split indicators
-  int s_indx[S];                 // Index of split points
-  matrix[T,K] X;                    // season vectors
-  real<lower=0> sigma;              // scale on seasonality prior
-  real<lower=0> tau;                  // scale on changepoints prior
-}
-
-
-transformed data {
-  int s_ext[S + 1];  // Segment endpoints
-  for (j in 1:S) {
-    s_ext[j] = s_indx[j];
-  }
-  s_ext[S + 1] = T + 1;  // Used for the m_adj loop below.
-}
-
-
-parameters {
-  real k;                            // Base growth rate
-  real m;                            // offset
-  vector[S] delta;                       // Rate adjustments
-  real<lower=0> sigma_obs;               // Observation noise (incl. seasonal variation)
-  vector[K] beta;                    // seasonal vector
-}
-
-
-transformed parameters {
-  vector[S] gamma;                  // adjusted offsets, for piecewise continuity
-  vector[S + 1] k_s;                 // actual rate in each segment
-  real m_pr;
-
-  // Compute the rate in each segment
-  k_s[1] = k;
-  for (i in 1:S) {
-    k_s[i + 1] = k_s[i] + delta[i];
-  }
-
-  // Piecewise offsets
-  m_pr = m; // The offset in the previous segment
-  for (i in 1:S) {
-    gamma[i] = (t[s_indx[i]] - m_pr) * (1 - k_s[i] / k_s[i + 1]);
-    m_pr = m_pr + gamma[i];  // update for the next segment
-  }
-}
-
-model {
-  //priors
-  k ~ normal(0, 5);
-  m ~ normal(0, 5);
-  delta ~ double_exponential(0, tau);
-  sigma_obs ~ normal(0, 0.1);
-  beta ~ normal(0, sigma);
-
-  // Likelihood
-  y ~ normal(cap ./ (1 + exp(-(k + A * delta) .* (t - (m + A * gamma)))) + X * beta, sigma_obs);
-}