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irb(main):012:0> account.logo.url
=> "/uploads/account/logo/1/me_climbing_mountains.jpg"

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- method: energy_cost_savings
  display: Energy Cost Savings
  tip: This is the tooltip

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    tooltips.keys.each do |key|
      define_method(key) { tooltip tooltips[key][:display], t[key][:tip] }
    end  

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spectral <- function(proximity, k = 2)
{
  D <- diag(apply(proximity, 1, sum))
  U <- D - proximity
  
  # L <- diag(nrow(my.data)) - solve(D) %*% A  # simple Laplacian
  # round(L[1:12,1:12],1)
  
  # matrix power operator: computes M^power (M must be diagonalizable)
  "%^%" <- function(M, power)

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mydata <- d
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
  for (i in 2:15) wss[i] <- sum(kmeans(mydata,
                                       centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares")

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# NBD with t distributed beta

beta.nbd.density <- function(x, r, alpha, a, b)
{
  # params:
  #   x: count we are evaluating density at
  #   r: nbd r parameter
  #   alpha: nbd alpha parameter
  #   a: beta alpha parameter
  #   b: beta beta parameter

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beta.nbd.ll <- function(x, n_x, r, alpha, a, b)
{
  # params:
  #   x: vector of counts
  #   n_x: number of observations for each count
  #   r, alpha: nbd params
  #   a, b: beta params
  # output:
  #   the log-likelihood of the model
  probs <- sapply(x, function(y) beta.nbd.density(y, r, alpha, a, b))

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library(ca)
library(ggplot2)
library(ggrepel)
library(stringr)
library(scales)
library(magrittr)
library(factoextra)

repel_ca_from_table <- 
  function(

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library(RColorBrewer)
library(fields) #to use designer.colors
library(reshape2)
library(dplyr)


gradient_heatmap <- 
  function(
    df, 
    num_percentiles = 10, 

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# Betas #

samples <- coda.samples(jags, c('beta_age',
                                'beta_percent_white',
                                'beta_uninsured',
                                'beta_unemployed',
                                'beta_percent_degree',
                                'beta_income',
                                'beta_health'), 10000)