Updates to ensure that the man pages show the examples

Author: jwbowers

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: DrBristol
-Title: Hypotheses Using Biased and Unbiased Urn Models Inspired by Muriel Bristol and Ronald Fisher
+Title: Hypothesis Tests Using Biased and Unbiased Urn Models Inspired by Muriel Bristol and Ronald Fisher
 Version: 0.0.1.0000
-Authors@R: c(person("Matias", "Lopez", role = c("aut", "cre"),  email="first.last@example.com", comment = c(ORCID = "YOUR-ORCID-ID")),
+Authors@R: c(person("Matias", "Lopez", role = c("aut", "cre"),  email="matiaslopez.uy@gmail.com", comment = c(ORCID = "0000-0002-0871-2841")),
       person("Jake", "Bowers", role ="aut", email = "jwbowers@illinois.edu", comment = c(ORCID = "0000-0002-4048-1166")))
 Description: This package implements the tests and sensitivity analyses described in Lopez and Bowers (20XX). Forthcoming. Title.
 License: MIT + file LICENSE

NAMESPACE

@@ -2,6 +2,5 @@
 
 export(find_p_two_types)
 export(sens_urn)
-export(sim_urn)
 importFrom(BiasedUrn,dFNCHypergeo)
 importFrom(stats,uniroot)

R/p_binary.R

@@ -15,6 +15,17 @@
 #' @param interpretation Logical. TRUE if the function returns text helping
 #' to interpret the result, FALSE (default option) to returns only the p-value
 #' @return Either a p-value (numeric, scalar) or a list containing the p-value and text containing an interpretation
+#' @examples
+#' # Equal probability, 2 kinds of evidence
+#' find_p_two_types(obs_support = 7, total_obs = 10)
+#' # Equal probability, 2 kinds of evidence with interpretation printed
+#' find_p_two_types(obs_support = 7, total_obs = 10, interpretation = TRUE)
+#' # Unequal probability, 2 kinds of evidence with interpretation printed
+#' find_p_two_types(obs_support = 7, total_obs = 10, interpretation = TRUE, odds = .5)
+#' find_p_two_types(obs_support = 7, total_obs = 10, interpretation = TRUE, odds = 2)
+#' # Equal probability, Unequal evidentiary weight, 2 kinds of evidence
+#' find_p_two_types(obs_support = 7, total_obs = 10, weights = rep(1, 7), interpretation = TRUE, odds = 1)
+#' find_p_two_types(obs_support = 7, total_obs = 10, weights = rep(c(2, 1), c(1, 7 - 1)), interpretation = TRUE, odds = 1)
 #' @export
 find_p_two_types <- function(obs_support, total_obs, odds = 1, weights = NULL, interpretation = FALSE) {
   if (is.null(weights)) {
@@ -45,15 +56,3 @@ find_p_two_types <- function(obs_support, total_obs, odds = 1, weights = NULL, i
     return(list(thep = thep, interp = interp))
   }
 }
-#' @examples
-#' ...
-#' # Equal probability, 2 kinds of evidence
-#' find_p_two_types(obs_support = 7, total_obs = 10)
-#' # Equal probability, 2 kinds of evidence with interpretation printed
-#' find_p_two_types(obs_support = 7, total_obs = 10, interpretation = TRUE)
-#' # Unequal probability, 2 kinds of evidence with interpretation printed
-#' find_p_two_types(obs_support = 7, total_obs = 10, interpretation = TRUE, odds = .5)
-#' find_p_two_types(obs_support = 7, total_obs = 10, interpretation = TRUE, odds = 2)
-#' # Equal probability, Unequal evidentiary weight, 2 kinds of evidence
-#' find_p_two_types(obs_support = 7, total_obs = 10, weights = rep(1, 7), interpretation = TRUE, odds = 1)
-#' find_p_two_types(obs_support = 7, total_obs = 10, weights = rep(c(2, 1), c(1, 7 - 1)), interpretation = TRUE, odds = 1)

R/plot_p.R

@@ -10,7 +10,15 @@
 #' @param total_obs An integer representing the total number of observations
 #' @param weights A vector of numeric weights representing the differential evidentiary weight of the working theory supporting observations.
 #' @param p_threshold A decimal (Double). The p-value threshold. Default is p=.05.
-#' @return The p-value for a given amount of bias in drawing observations from the urn.
+#' @return A list with two elements:
+#' (1) in \code{w} the differential odds of observing a working theory supporting observation over a rival theory supporting observation.
+#' (2) in \code{p} The p-value given the found \code{w}.
+#' @examples
+#' # What is the odds that would bring our p=.02 to p \approx .05
+#' find_p_two_types(obs_support = 7, total_obs = 10, odds = 1)
+#' sens_urn(obs_support = 7, total_obs = 10, p_threshold = .05)$w
+#' # Notice that this is correct:
+#' find_p_two_types(obs_support = 7, total_obs = 10, odds = 1.58928)
 #' @importFrom BiasedUrn dFNCHypergeo
 #' @importFrom stats uniroot
 #' @export
@@ -39,9 +47,3 @@ sens_urn <- function(obs_support, total_obs, weights = NULL, p_threshold = .05)
   )
   return(list(w = theodds, p = thep_at_theodds))
 }
-#' @examples
-#' # What is the odds that would bring our p=.02 to p \approx .05
-#' find_p_two_types(obs_support = 7, total_obs = 10, odds = 1)
-#' sens_urn(obs_support = 7, total_obs = 10, p_threshold = .05)$w
-#' # Notice that this is correct:
-#' find_p_two_types(obs_support = 7, total_obs = 10, odds = 1.58928)