Merge pull request #40 from jonlachmann/pre-cran

Pre cran

Author: jonlachmann

DESCRIPTION

@@ -13,11 +13,12 @@ Description: Implements the Mode Jumping Markov Chain Monte Carlo algorithm desc
 URL: https://github.com/jonlachmann/FBMS
 License: GPL-2
 Depends:
-    R (>= 3.5.0),
+    R (>= 3.5.0)
 Imports:
     Rcpp,
     fastglm,
     GenSA,
+    parallel,
     methods,
     r2r,
     BAS,

NAMESPACE

@@ -6,7 +6,6 @@ S3method(coef,gmjmcmc)
 S3method(coef,gmjmcmc_merged)
 S3method(coef,mjmcmc)
 S3method(coef,mjmcmc_parallel)
-S3method(fitted,fbms_predict)
 S3method(get.best.model,gmjmcmc)
 S3method(get.best.model,gmjmcmc_merged)
 S3method(get.best.model,mjmcmc)
@@ -51,6 +50,7 @@ export(erf)
 export(exp_dbl)
 export(fbms)
 export(fbms.mlik.master)
+export(fitted.fbms_predict)
 export(gaussian.loglik)
 export(gelu)
 export(gen.params.gmjmcmc)

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/fbms.R

@@ -1,6 +1,5 @@
-#' Fit a BGNLM  model using Genetically Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling.
-#' Or Fit a BGLM model using Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling.
-#' 
+#' Fit a BGNLM Model Using MJMCMC or GMJMCMC Sampling.
+#'
 #' This function fits a model using the relevant MCMC sampling. The user can specify the formula,
 #' family, data, transforms, and other parameters to customize the model.
 #'

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

@@ -3,8 +3,9 @@
 # Created by: jonlachmann
 # Created on: 2021-02-11
 
-#' 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.
+#' 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.
 #'
 #' @param transforms The vector of non-linear transformations
 #'
@@ -38,7 +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.
@@ -61,7 +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
 #'