swiftsam

library(ggplot2)
library(data.table)
library(scales)


ct.binned <- overp.indiv.ct[, list(confidence = mean(MeanMaxFcast),
                                   hit_rate   = mean(HitRate),
                                   ci_hit_rate = 1.96*sd(HitRate)/sqrt(.N),
                                   count      = .N),
                            by=c("bin,ct")]

swiftsam

####~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
### Cache (memoization) utilities
###
### Purpose
###  * Functions to save the output of time consuming functions to memory and
###    return results quickly if we already have the answers
### 
### Notes: Standard usage would go like this
### FunctionName <- function(){
###   args          <- c(as.list(environment()), list())

swiftsam

user.libs <- row.names(installed.packages(lib.loc = .libPaths()[1]))
sys.libs  <- row.names(installed.packages(lib.loc = .libPaths()[2]))

remove.packages(intersect(sys.libs,user.libs), lib=.libPaths()[2])

swiftsam

###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
### calculates Brier Score for a single forecast on an Ordered IFP
### calculation based on definition in Appendix B of ACE_TE_Plan_OY1_v3.
### procedure reduces penalty to probability assigned to bins adjacent
### to the realized outcome
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ScoreOrdered <- function(fcast.values, 
                         correct.values,
                         scoring.fn){
  # Step 1 – Take the original categories (A-B-C-D) and break them up