Port package dev version over to fresh repo

..Rcheck/00check.log

@@ -0,0 +1,9 @@
+* using log directory ‘/home/cantabile/Documents/repos/aggreCAT/..Rcheck’
+* using R version 3.6.2 (2019-12-12)
+* using platform: x86_64-pc-linux-gnu (64-bit)
+* using session charset: UTF-8
+* checking for file ‘./DESCRIPTION’ ... ERROR
+Required fields missing or empty:
+  ‘Author’ ‘Maintainer’
+* DONE
+Status: 1 ERROR

.Rbuildignore

@@ -0,0 +1,25 @@
+^aggreCAT\.Rproj$
+^\.Rproj\.user$
+^LICENSE\.md$
+^README\.Rmd$
+^\.travis\.yml$
+^codemeta\.json$
+^\.github$
+^codecov\.yml$
+^archived$
+^data-raw$
+^resources$
+^analysis$
+^data-export$
+^data-anon$
+^aggreCAT$
+Mixed_Methods_Analysis.html
+Mixed_Methods_Analysis.Rmd
+SIPS_ArMean_figure.png
+SIPS_cs.csv
+^doc$
+^Meta$
+vignettes/test_table.Rmd
+^ms$
+^test_tble$
+^test$

.gitattributes

@@ -0,0 +1,5 @@
+docs/* linguist-detectable=false
+*.html linguist-detectable=false
+*.css linguist-detectable=false
+*.js linguist-detectable=false
+*.tex linguist-detectable=false

DESCRIPTION

@@ -0,0 +1,93 @@
+Package: aggreCAT
+Title: Mathematically Aggregating Expert Judgments
+Version: 0.0.0.9000
+Authors@R: c(person(given = "Aaron", 
+                    family = "Willcox", 
+                    role = "aut",
+                    email = " [email protected]",
+                    comment = structure("0000-0003-2536-2596", .Names = "ORCID")),
+                person(given = "Charles",
+                    family = "Gray",
+                    role = c("aut"),
+                    comment = structure("00000-0002-9978-011X", .Names = "ORCID")),
+                person(given = "Elliot", 
+                    family = "Gould", 
+                    role = "aut", 
+                    comment = structure("0000-0002-6585-538X", .Names = "ORCID")),
+                person(given = "David", 
+                        family = "Wilkinson", 
+                        role = c("aut", "cre"),
+                        email = "[email protected]",
+                        comment = structure("0000-0002-9560-6499", .Names = "ORCID")
+                        ),
+                person(given = "Anca", 
+                    family = "Hanea", 
+                    role = "aut", 
+                    comment = structure("0000-0003-3870-5949", .Names = "ORCID")),
+                person(given = "Bonnie", 
+                    family = "Wintle", 
+                    role = "aut", 
+                    comment = structure("0000-0003-0236-6906", .Names = "ORCID")),
+                person(given = "Rose", 
+                    family = "E. O'Dea", 
+                    role = "aut", 
+                    comment = structure("0000-0001-8177-5075", .Names = "ORCID"))
+           )
+Description: Aggregator methods for confidence scores.
+URL: https://replicats.research.unimelb.edu.au/
+License: MIT + file LICENSE
+Encoding: UTF-8
+LazyData: true
+Roxygen: list(markdown = TRUE)
+Suggests: 
+    testthat (>= 2.1.0),
+    knitr,
+    rmarkdown,
+    covr,
+    pointblank,
+    janitor,
+    DescTools,
+    qualtRics,
+    here,
+    readxl,
+    readr,
+    stats,
+    lubridate,
+    forcats,
+    ggforce, 
+    ggpubr,
+    ggridges, 
+    rjags,
+    tidybayes,
+    tidyverse,
+    usethis,
+    nlme,
+    gt, 
+    gtExtras
+RoxygenNote: 7.2.1
+Depends:
+    R (>= 2.10)
+Imports: 
+    magrittr,
+    GoFKernel,
+    purrr,
+    R2jags,
+    coda,
+    precrec,
+    mathjaxr,
+    cli,
+    VGAM,
+    crayon,
+    dplyr, 
+    rfUtilities,
+    stringr,
+    tidyr, 
+    tibble,
+    ggplot2,
+    insight
+Remotes: 
+    softloud/neet
+VignetteBuilder: knitr
+RdMacros: mathjaxr
+Config/testthat/parallel: true
+Config/testthat/edition: 3

LICENSE

@@ -0,0 +1,2 @@
+YEAR: 2020
+COPYRIGHT HOLDER: Charles Gray

LICENSE.md

@@ -0,0 +1,21 @@
+# MIT License
+
+Copyright (c) 2020 Charles Gray
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NAMESPACE

@@ -0,0 +1,28 @@
+# Generated by roxygen2: do not edit by hand
+
+export("%>%")
+export(AverageWAgg)
+export(BayesianWAgg)
+export(DistributionWAgg)
+export(ExtremisationWAgg)
+export(IntervalWAgg)
+export(LinearWAgg)
+export(ReasoningWAgg)
+export(ShiftingWAgg)
+export(confidence_score_evaluation)
+export(confidence_score_heatmap)
+export(confidence_score_ridgeplot)
+export(method_placeholder)
+export(postprocess_judgements)
+export(preprocess_judgements)
+export(weight_asym)
+export(weight_interval)
+export(weight_nIndivInterval)
+export(weight_outlier)
+export(weight_reason)
+export(weight_reason2)
+export(weight_varIndivInterval)
+importFrom(insight,format_capitalize)
+importFrom(magrittr,"%>%")
+importFrom(stats,median)
+importFrom(stats,var)

R/AverageWAgg.R

@@ -0,0 +1,174 @@
+#' @title
+#' Aggregation Method: AverageWAgg
+#'
+#' @description
+#' Calculate one of several types of averaged best estimates.
+#'
+#' @details
+#' This function returns the average, median and transformed averages of
+#' best-estimate judgements for each claim.
+#'
+#' `type` may be one of the following:
+#' \loadmathjax
+#'
+#' **ArMean**: Arithmetic mean of the best estimates
+#' \mjdeqn{\hat{p}_c\left(ArMean \right ) = \frac{1}{N}\sum_{i=1}^N B_{i,c}}{ascii}
+#' **Median**: Median of the best estimates
+#' \mjdeqn{\hat{p}_c \left(\text{median} \right) = \text{median} \{ B^i_c\}_{i=1,...,N}}{ascii}
+#' **GeoMean**: Geometric mean of the best estimates
+#' \mjdeqn{GeoMean_{c}=  \left(\prod_{i=1}^N  B_{i,c}\right)^{\frac{1}{N}}}{ascii}
+#' **LOArMean**: Arithmetic mean of the log odds transformed best estimates
+#' \mjdeqn{LogOdds_{i,c}= \frac{1}{N} \sum_{i=1}^N log\left( \frac{B_{i,c}}{1-B_{i,c}}\right)}{ascii}
+#' The average log odds estimate is then back transformed to give a final group estimate:
+#' \mjdeqn{\hat{p}_c\left( LOArMean \right) = \frac{e^{LogOdds_{i,c}}}{1+e^{LogOdds_{i,c}}}}{ascii}
+#' **ProbitArMean**: Arithmetic mean of the probit transformed best estimates
+#' \mjdeqn{Probit_{c}= \frac{1}{N} \sum_{i=1}^N \Phi^{-1}\left( B_{i,c}\right)}{ascii}
+#' The average probit estimate is then back transformed to give a final group estimate:
+#' \mjdeqn{\hat{p}_c\left(ProbitArMean \right) = \Phi\left({Probit_{c}}\right)}{ascii}
+#'
+#' @param expert_judgements A dataframe in the format of [data_ratings].
+#' @param type One of `"ArMean"`, `"Median"`, `"GeoMean"`, `"LOArMean"`, or `"ProbitArMean"`.
+#' @param name Name for aggregation method. Defaults to `type` unless specified.
+#' @param placeholder Toggle the output of the aggregation method to impute placeholder data.
+#' @param percent_toggle Change the values to probabilities. Default is `FALSE`.
+#'
+#' @return A tibble of confidence scores `cs` for each `paper_id`.
+#'
+#' @examples
+#' \dontrun{AverageWAgg(data_ratings)}
+#'
+#' @export
+#' @md
+
+AverageWAgg <- function(expert_judgements,
+                        type = "ArMean",
+                        name = NULL,
+                        placeholder = FALSE,
+                        percent_toggle = FALSE) {
+
+  if(!(type %in% c("ArMean",
+                   "GeoMean",
+                   "Median",
+                   "LOArMean",
+                   "LOGeoMean",
+                   "ProbitArMean"))){
+
+    stop('`type` must be one of "ArMean", "GeoMean", "Median", "LOArMean", "LOGeoMean", or "ProbitArMean"')
+
+  }
+
+  ## Set name argument
+
+  name <- ifelse(is.null(name),
+                 type,
+                 name)
+
+  cli::cli_h1(sprintf("AverageWAgg: %s",
+                      name))
+
+  if(isTRUE(placeholder)){
+
+    method_placeholder(expert_judgements,
+                       name)
+
+  } else {
+
+    df <- expert_judgements %>%
+      preprocess_judgements(percent_toggle = {{percent_toggle}}) %>%
+      dplyr::filter(element == "three_point_best") %>%
+      dplyr::group_by(paper_id)
+
+    switch(type,
+           "ArMean" = {
+
+             df <- df %>%
+               dplyr::summarise(
+                 aggregated_judgement = mean(value,
+                                             na.rm = TRUE),
+                 n_experts = dplyr::n()
+               )
+
+           },
+           "GeoMean" = {
+
+             df <- df %>%
+               dplyr::summarise(n_experts = dplyr::n(),
+                                aggregated_judgement = (prod(value, na.rm = TRUE)) ^ (1/n_experts))
+
+           },
+           "Median" = {
+
+             df <- df %>%
+               dplyr::summarise(
+                 aggregated_judgement = median(value,
+                                               na.rm = TRUE),
+                 n_experts = dplyr::n()
+               )
+
+           },
+           "LOArMean" = {
+
+             if(any(df$value < 0) | any(df$value > 1)){
+
+               stop("LOArMean requires probabilistic judgements. Check your data compatability or `percent_toggle` argument.")
+
+             }
+
+             df <- df %>%
+               dplyr::mutate(value = dplyr::case_when(value == 1 ~ value - .Machine$double.eps,
+                                                      value == 0 ~ value + .Machine$double.eps,
+                                                      TRUE ~ value),
+                             log_odds = log(abs(value / (1 - value)))) %>%
+               dplyr::summarise(
+                 aggregated_judgement = mean(log_odds,
+                                             na.rm = TRUE),
+                 n_experts = dplyr::n()
+               )  %>%
+               dplyr::mutate(
+                 aggregated_judgement = exp(aggregated_judgement) / (1 + exp(aggregated_judgement))
+               )
+
+           },
+           "LOGeoMean" = {
+
+             if(any(df$value < 0) | any(df$value > 1)){
+
+               stop("LOGeoMean requires probabilistic judgements. Check your data compatability or `percent_toggle` argument.")
+
+             }
+
+             df <- df %>%
+               dplyr::mutate(value = dplyr::case_when(value == 1 ~ value - .Machine$double.eps,
+                                                      value == 0 ~ value + .Machine$double.eps,
+                                                      value == 0.5 ~ value + .Machine$double.eps,
+                                                      TRUE ~ value),
+                             log_odds = log(abs(value / (1 - value)))) %>%
+               # dplyr::summarise(n_experts = dplyr::n(),
+               #                  aggregated_judgement = (prod(log_odds, na.rm = TRUE))) %>%
+               dplyr::summarise(n_experts = dplyr::n(),
+                                aggregated_judgement = (prod(log_odds, na.rm = TRUE)) ^ (1/n_experts)) %>%
+               dplyr::mutate(
+                 aggregated_judgement = exp(aggregated_judgement) / (1 + exp(aggregated_judgement))
+               )
+
+           },
+           "ProbitArMean" = {
+
+
+             df <- df %>%
+               dplyr::mutate(probit = VGAM::probitlink(value,
+                                                       bvalue = .Machine$double.eps)) %>%
+               dplyr::summarise(aggregated_judgement = mean(probit,
+                                                            na.rm = TRUE),
+                                n_experts = dplyr::n()) %>%
+               dplyr::mutate(aggregated_judgement = VGAM::probitlink(aggregated_judgement,
+                                                                     inverse = TRUE))
+
+           })
+
+    df %>%
+      dplyr::mutate(method = name) %>%
+      postprocess_judgements()
+
+  }
+}