* Set title on all man page titles.

Author: jonlachmann

R/abalone-data.R

@@ -1,4 +1,4 @@
-#' Physical measurements of 4177 abalones, a species of sea snail.
+#' Physical Measurements of 4177 Abalones, a Species of Sea Snail.
 #'
 #' %% ~~ A concise (1-5 lines) description of the dataset. ~~
 #'

R/alpha_generation.R

@@ -9,7 +9,7 @@ gen.alphas <- function (strategy, feature, data, loglik, verbose) {
   return(feature)
 }
 
-#' Alpha generator using strategy 1 as per Hubin et. al.
+#' Alpha Generator Using Strategy 1 as per Hubin et. al.
 #' TODO: This is just a placeholder.
 #'
 #' @noRd
@@ -19,7 +19,7 @@ alpha_1 <- function (feature) {
   return(feature)
 }
 
-#' Alpha generator using strategy 2 as per Hubin et. al.
+#' Alpha Generator Using Strategy 2 as per Hubin et. al.
 #' TODO: This is just a placeholder.
 #'
 #' @noRd
@@ -29,7 +29,7 @@ alpha_2 <- function (feature) {
   return(feature)
 }
 
-#' Alpha generator using strategy 3 as per Hubin et. al.
+#' Alpha Generator Using Strategy 3 as per Hubin et. al.
 #'
 #' @param feature The feature to generate alphas for
 #' @param data The dataset used
@@ -65,7 +65,7 @@ alpha_3 <- function (feature, data, loglik, verbose) {
   return(feature)
 }
 
-#' Alpha generator using strategy 4 as per Hubin et. al.
+#' Alpha Generator Using Strategy 4 as per Hubin et. al.
 #'
 #' @param feature The feature to generate alphas for
 #'

R/arguments.R

@@ -3,7 +3,7 @@
 # Created by: jonlachmann
 # Created on: 2021-02-19
 
-#' Generate a probability list for MJMCMC (Mode Jumping MCMC)
+#' Generate a Probability List for MJMCMC (Mode Jumping MCMC)
 #'
 #' @return A named list with five elements:
 #' \describe{
@@ -41,7 +41,7 @@ gen.probs.mjmcmc <- function () {
   return(probs)
 }
 
-#' Generate a probability list for GMJMCMC (Genetically Modified MJMCMC)
+#' Generate a Probability List for GMJMCMC (Genetically Modified MJMCMC)
 #'
 #' @param transforms A list of the transformations used (to get the count).
 #'
@@ -119,7 +119,7 @@ gen.probs.gmjmcmc <- function (transforms) {
   return(probs)
 }
 
-#' Generate a parameter list for MJMCMC (Mode Jumping MCMC)
+#' Generate a Parameter List for MJMCMC (Mode Jumping MCMC)
 #'
 #' @param ncov The number of covariates in the dataset that will be used in the algorithm
 #'
@@ -216,7 +216,7 @@ gen.params.mjmcmc <- function (ncov) {
   return(params)
 }
 
-#' Generate a parameter list for GMJMCMC (Genetically Modified MJMCMC)
+#' Generate a Parameter List for GMJMCMC (Genetically Modified MJMCMC)
 #'
 #' This function generates the full list of parameters required for the Generalized Mode Jumping Markov Chain Monte Carlo (GMJMCMC) algorithm, building upon the parameters from \code{gen.params.mjmcmc}. The generated parameter list includes feature generation settings, population control parameters, and optimization controls for the search process.
 #'

R/exa-data.R

@@ -1,4 +1,4 @@
-#' Excerpt from the Open Exoplanet Catalogue data set
+#' Excerpt from the Open Exoplanet Catalogue Data Set
 #'
 #' Data fields include planet and host star attributes.
 #'
@@ -32,4 +32,4 @@
 #' Toronto, Ontario M1C 1A4
 #' hanno.rein 'at' utoronto.ca
 #'
-"exoplanet"
+"exoplanet"

R/feature.R

@@ -16,7 +16,7 @@
 # | NA          FEAT3 ALPHA3 |
 # ...
 
-#' Create method for "feature" class
+#' Create Method for \"feature\" Class
 #'
 #' @param transform A numeric denoting the transform type
 #' @param features A list of features to include
@@ -58,7 +58,7 @@ create.feature <- function (transform, features, trans.priors, alphas=NULL) {
   return(feature)
 }
 
-#' Update alphas on a feature
+#' Update Alphas on a Feature
 #'
 #' @param feature The feature to be updated
 #' @param alphas The alphas that will be used
@@ -93,7 +93,7 @@ update.alphas <- function (feature, alphas, recurse=FALSE) {
   else return(feature)
 }
 
-#' Print method for "feature" class
+#' Print Method for \"feature\" Class
 #'
 #' @param x An object of class "feature"
 #' @param dataset Set the regular covariates as columns in a dataset

R/gmjmcmc.R

@@ -8,7 +8,7 @@
 #' @importFrom Rcpp sourceCpp
 NULL
 
-#' Main algorithm for GMJMCMC (Genetically Modified MJMCMC)
+#' Main Algorithm for GMJMCMC (Genetically Modified MJMCMC)
 #'
 #' @param x matrix containing the design matrix with data to use in the algorithm
 #' @param y response variable
@@ -202,7 +202,7 @@ gmjmcmc <- function (
 }
 
 
-#' Subalgorithm for generating a new population of features in GMJMCMC (Genetically Modified MJMCMC)
+#' Subalgorithm for Generating a New Population of Features in GMJMCMC (Genetically Modified MJMCMC)
 #'
 #' @param S.t The current population of features
 #' @param F.0 The initial population of features, i.e. the bare covariates
@@ -294,4 +294,3 @@ gmjmcmc.transition <- function (S.t, F.0, data, loglik.alpha, marg.probs.F.0, ma
   }
   return(S.t)
 }
-

R/gmjmcmc_support.R

@@ -3,7 +3,7 @@
 # Created by: jonlachmann
 # Created on: 2021-02-11
 
-#' Set the transformations option for GMJMCMC (Genetically Modified MJMCMC).
+#' Set the Transformations Option for GMJMCMC (Genetically Modified MJMCMC).
 #'
 #' This is also done when running the algorithm, but this function allows for it to be done manually.
 #'
@@ -39,7 +39,7 @@ verify.inputs <- function (data, loglik.pi, transforms, T, N, N.final, probs, pa
   if (sum(probs$large.kern > 1 | probs$large.kern < 0) != 0) error <- c(error, "Large jump kernel probabilities must be in [0,1].")
 }
 
-#' Function for calculating marginal inclusion probabilities of features given a list of models
+#' Function for Calculating Marginal Inclusion Probabilities of Features Given a List of Models
 #' @param models The list of models to use.
 #'
 #' @return A numeric vector of marginal model probabilities based on relative frequencies of model visits in MCMC.
@@ -62,7 +62,7 @@ marginal.probs <- function (models) {
   return(probs)
 }
 
-#' Function for calculating feature importance through renormalized model estimates
+#' Function for Calculating Feature Importance Through Renormalized Model Estimates
 #' @param models The models to use.
 #' @param type Select which probabilities are of interest, features or models
 #' 

R/impute.R

@@ -1,5 +1,5 @@
 
-#' Impute Missing Values the Data
+#' Impute Missing Values in the Data
 #'
 #' Imputes missing values in the data using median imputation based on the data  set.
 #'
@@ -80,4 +80,4 @@ impute_x_pred <- function (object, x_test, x_train) {
     return(as.matrix(data.frame(df,na.matr)))
   }
   return(as.matrix(x_test))
-}
+}

R/likelihoods.R

@@ -4,7 +4,7 @@
 # Created on: 2021-02-24
 
 
-#' Log likelihood function for logistic regression with a Jeffreys parameter prior and BIC approximations of the posterior.
+#' Log Likelihood Function for Logistic Regression with a Jeffreys Parameter Prior and BIC Approximations of the Posterior.
 #'
 #' This function is created as an example of how to create an estimator that is used
 #' to calculate the marginal likelihood of a model.
@@ -237,7 +237,7 @@ loadLmBas <- function() {
 }
 
 
-#' Log likelihood function for gaussian regression with a Jeffreys prior and BIC approximation
+#' Log Likelihood Function for Gaussian Regression with a Jeffreys Prior and BIC Approximation
 #'
 #' @param y A vector containing the dependent variable
 #' @param x The matrix containing the precalculated features
@@ -479,7 +479,7 @@ gaussian.loglik.alpha <- function (a, data, mu_func) {
 }
 
 
-#' Log model prior function
+#' Log Model Prior Function
 #' @param mlpost_params list of passed parameters of the likelihood in GMJMCMC
 #' @param complex list of complexity measures of the features included into the model
 #'
@@ -577,4 +577,4 @@ fbms.mlik.master <- function(y, x, model, complex, mlpost_params = list(family =
   result <- loglik.pi(y, x, model, complex, params_use)
 
   return(list(crit = result$crit, coefs = result$coefs))
-}
+}

R/mjmcmc.R

@@ -3,7 +3,7 @@
 # Created by: jonlachmann
 # Created on: 2021-04-27
 
-#' Main algorithm for MJMCMC (Genetically Modified MJMCMC)
+#' Main Algorithm for MJMCMC (Genetically Modified MJMCMC)
 #'
 #' @param x matrix containing the design matrix with data to use in the algorithm,
 #' @param y response variable
@@ -109,7 +109,7 @@ mjmcmc <- function (
   return(result)
 }
 
-#' The main loop for the MJMCMC (Mode Jumping MCMC) algorithm, used in both MJMCMC and GMJMCMC (Genetically Modified MJMCMC)
+#' The Main Loop for the MJMCMC (Mode Jumping MCMC) Algorithm, Used in Both MJMCMC and GMJMCMC (Genetically Modified MJMCMC)
 #'
 #' @param data The data to use
 #' @param complex The complexity measures of the data
@@ -195,7 +195,7 @@ mjmcmc.loop <- function (data, complex, loglik.pi, model.cur, N, probs, params,
   ))
 }
 
-#' Subalgorithm for generating a proposal and acceptance probability in (G)MJMCMC
+#' Subalgorithm for Generating a Proposal and Acceptance Probability in (G)MJMCMC
 #'
 #' @param data The data to use in the algorithm
 #' @param loglik.pi The the (log) density to explore