ToxicR 1.0.1

DESCRIPTION

@@ -1,24 +1,25 @@
 Package: ToxicR
 Type: Package
-Title: This package is for analyzing toxicology dose-response data. 
-Version: 22.01
-Date: 2022-1-31
+Title: Analyzing Toxicology Dose-Response Data in R
+Version: 22.5
+Date: 2022-05-31
 Author: NIEHS-BCBB 
 Maintainer: Matt Wheeler <matt.wheeler@nih.gov>
-Description: This package provides standard toxicology routines for analyzing
+Description:  Standard toxicology routines for analyzing
     dose-response data, which include: dose-response analysis, trend tests, 
-    and Bayesian sensitivity analyses. 
+    and Bayesian sensitivity analyses. For more information, 
+    go to the website <https://github.com/NIEHS/ToxicR>. 
 Depends:
     R (>= 4.1.0)
-License: MIT 
+License: MIT + file LICENSE 
 LazyData: true
-Imports: Rcpp (>= 1.0.0), ggplot2 (>= 3.3.2), shiny (>= 1.5.0), plotly (>= 4.9.2.1),
+Imports: Rcpp (>= 1.0.0), ggplot2 (>= 3.3.2), shiny (>= 1.5.0),  coda (>= 0.19-4),
          scales (>= 1.1.1), tidyverse (>= 1.3.0), forcats, ggridges (>= 0.5.3), doBy (>= 4.6.11),
-         modules , multcomp (>= 1.4), VIM (>= 6.1.1), gridExtra (>= 2.3), knitr (>= 1.36), 
-         dplyr (>= 1.0.7), ggpubr (>= 0.4.0), bibtex (>= 0.4.2.3), testthat (>= 3.1.0), actuar (>= 3.2-0)
-Include: RcppEigen, RcppGSL 
+         multcomp (>= 1.4), dplyr (>= 1.0.7) 
 LinkingTo: Rcpp, RcppEigen, RcppGSL
 RoxygenNote: 7.1.2
 VignetteBuilder: knitr
 Suggests: 
-    rmarkdown
+    rmarkdown, actuar (>= 3.2-0),ggpubr (>= 0.4.0), testthat (>= 3.1.0),gridExtra (>= 2.3),
+    VIM (>= 6.1.1), knitr (>= 1.36), modules, plotly (>= 4.9.2.1)
+

LICENSE

@@ -1,6 +1,4 @@
-MIT License
-
-Copyright (c) 2022 Matthew Wheeler
+Copyright (c) 2022 <NIEHS matt.wheeler@nih.gov>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

NAMESPACE

@@ -7,13 +7,20 @@ importFrom(forcats,fct_reorder)
 # Necessary Packages
 ###############################
 import(tidyverse)
-#importFrom(doBy, summaryBy)
-import(doBy)
+
+importFrom(doBy, summaryBy)
+#import(doBy)
 import(multcomp)
 import(shiny)
 import(scales)
 import(ggplot2)
-import(dplyr)
+importFrom(dplyr,"filter","%>%","select","mutate")
+
+
+
+importFrom("stats", "aov", "as.formula", "coefficients", "cor.test",
+             "density", "lm", "pchisq", "pgamma", "pnorm", "qnorm",
+             "quantile", "sd", "splinefun", "var")
 
 # Added two additional package 2012/12/08 SL
 import(ggridges)
@@ -22,17 +29,37 @@ import(ggridges)
 #register all of the S3 methods for the print commands
 #and the plot commands etc. 
 
-S3method(print, BMD_Bayes_dichotomous_model, .print.BMD_Bayes_model)
-S3method(print, BMD_Bayes_continuous_model, .print.BMD_Bayes_model)
-S3method(print, BMD_CDF, .print.BMD_CDF)
-S3method(print, BMDdich_fit, .print.BMDdich_fit)
+#S3method(print, BMD_Bayes_dichotomous_model, .print.BMD_Bayes_model)
+#S3method(print, BMD_Bayes_continuous_model, .print.BMD_Bayes_model)
+#S3method(print, BMD_CDF, .print.BMD_CDF)
+#S3method(print, BMDdich_fit, .print.BMDdich_fit)
 
 # Summary Methods
 S3method(summary, BMDcont_fit_maximized ,.summary_continuous_max)
 S3method(print,summary_continuous_max  ,.print_summary_continuous_max)
 S3method(summary, BMDdich_fit_maximized ,.summary_dichotomous_max)
 S3method(print,summary_dichotomous_max  ,.print_summary_dichotomous_max)
 
+S3method(summary,BMDcont_fit_MCMC , .summary_continuous_mcmc)
+S3method(summary,BMDdich_fit_MCMC , .summary_continuous_mcmc)
+S3method(print,summary_mcmc  , .print_summary_continuous_mcmc)
+
+S3method(summary,BMDcontinuous_MA_laplace , .summary_ma_max)
+S3method(summary,BMDdichotomous_MA_laplace , .summary_ma_max)
+S3method(print,ma_summary_max,.print_summary_ma_max )
+
+S3method(summary , ntp.shirley,  .summary_ntpshirley)
+S3method(summary , ntp.williams, .summary_ntpwilliams)
+S3method(summary , ntp.dunn,  .summary_ntpdunn)
+S3method(summary , ntp.dunnett, .summary_ntpdunnett)
+#Predict Methods
+
+S3method(predict,BMDdich_fit_maximized, .dichotomous_predict_model)
+S3method(predict,BMDcont_fit_maximized, .continuous_predict_model)
+S3method(predict,BMDcont_fit_MCMC, .continuous_predict_model_mcmc)
+S3method(predict,BMDdich_fit_MCMC, .dichotomous_predict_model_mcmc)
+
+
 # Base plot for single case
 
 S3method(plot,  BMDdich_fit_MCMC, .plot.BMDdich_fit_MCMC)

R/Build_Priors.R

@@ -17,7 +17,7 @@
 #CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
 #OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
-parse_prior<-function(prior){
+.parse_prior<-function(prior){
   rV <-list()
   rV$prior <- prior$prior
 
@@ -33,6 +33,23 @@ parse_prior<-function(prior){
 
 }
 
+#' @title create_continuous_prior  Given priorlist, a model, 
+#'        and a distribution. Create a prior for a given analysis. 
+#' @param prior_list First Prior
+#' @param model Model to be used
+#' @param distribution - Normal "normal", Normal non-constant variance "normal-ncv", or
+#'                       log-normal "lognormal"
+#' @param deg - For polynomial models only, the degree of the polynomial. 
+#' @return new BMDprior list. 
+#' 
+#' @examples 
+#' plist<- create_prior_list(normprior(0,0.1,-100,100), # a
+#'                           normprior(0,1,  -1e2,1e2),     # b
+#'                           lnormprior(1,0.2,0,18),  #k
+#'                           normprior(0,1,-18,18))
+#'  
+#'  power_normal <- create_continuous_prior(plist,"power","normal") 
+#'
 create_continuous_prior <- function( prior_list,model,distribution,deg=2){
 
   if (class(prior_list) != "BMDmodelprior"){
@@ -376,6 +393,19 @@ create_continuous_prior <- function( prior_list,model,distribution,deg=2){
   return(prior)
 }
 
+
+#' @title create_dichotomous_prior  Given priorlist, a model, 
+#'        and a distribution. Create a prior for a given analysis. 
+#' @param prior First Prior
+#' @param model Model to be used should be one of"hill","gamma","logistic","log-logistic","log-probit","multistage", "probit", "qlinear", or "weibull" 
+#' @return new BMDprior list that can be used in a dichotomous fit. 
+#' 
+#' @examples 
+#' plist<- create_prior_list(normprior(0,0.1,-100,100), # a
+#'                           lnormprior(1,0.2,0,18))
+#'  
+#'  power_normal <- create_dichotomous_prior(plist,"logistic") 
+#'
 create_dichotomous_prior <- function(prior,model){
 
   if (class(prior) != "BMDmodelprior"){

R/MAdensity_plot.R

@@ -5,20 +5,24 @@
 #' @param A the model averaged model to plot
 #' @examples 
 #' \dontrun{
-#' model <- ma_continuous_fit(doses,y,model_list=model_list,
+#' doses  <- cbind(c(0,25,50,100,200))
+#' y <- cbind(c(6,5.2,2.4,1.1,0.75),
+#'                 c(20,20,19,20,20),
+#'                 c(1.2,1.1,0.81,0.74,0.66))
+#' model <- ma_continuous_fit(doses,y,
 #'                         fit_type = "mcmc",BMD_TYPE = 'sd',BMR = 1)
 #' MAdensity_plot(model)
 #' }
 #' @export
-MAdensity_plot <- function (A, ...){
+MAdensity_plot <- function (A){
   #source("dicho_functions.R")
   UseMethod("MAdensity_plot")
 }
 
 # Sample Dichotomous Data set
-
 .plot.density.BMDdichotomous_MA_MCMC<-function(A){
 # Construct bmd sample plots for mcmc
+  X1 <- X2 <- X3 <- NULL
   class_list <- names(A)
   fit_idx    <- grep("Individual_Model",class_list)
   qprob=0.05
@@ -257,7 +261,7 @@ MAdensity_plot <- function (A, ...){
 # No we don't need this part
 
 .plot.density.BMDdichotomous_MA_maximized<-function(A){
-
+  t_1 <- t_2 <- t_3 <- t_4 <- t_5 <- t_6 <- t_7 <- t_8 <- t_9 <- c3 <- X1 <- X2 <- X3 <- NULL
   class_list <- names(A)
 
   if (class(A)[2]=="BMDdichotomous_MA_maximized"){
@@ -384,6 +388,7 @@ MAdensity_plot <- function (A, ...){
 
 .plot.density.BMDcontinous_MA_MCMC<-function(A){
   # Construct bmd sample plots for mcmc
+  X1 <- X2 <- X3 <- NULL
   class_list <- names(A)
   fit_idx    <- grep("Individual_Model",class_list)
   qprob=0.05
@@ -406,23 +411,23 @@ MAdensity_plot <- function (A, ...){
 
 
     if (fit$model=="hill"){
-      Q <- apply(fit$mcmc_result$PARM_samples,1,cont_hill_f, d=test_doses)
+      Q <- apply(fit$mcmc_result$PARM_samples,1,.cont_hill_f, d=test_doses)
 
     }
     if (fit$model=="exp-3"){
-      Q <- apply(fit$mcmc_result$PARM_samples,1,cont_exp_3_f, d=test_doses)
+      Q <- apply(fit$mcmc_result$PARM_samples,1,.cont_exp_3_f, d=test_doses)
 
     }
     if (fit$model=="exp-5"){
-      Q <- apply(fit$mcmc_result$PARM_samples,1,cont_exp_5_f, d=test_doses)
+      Q <- apply(fit$mcmc_result$PARM_samples,1,.cont_exp_5_f, d=test_doses)
 
     }
     if (fit$model=="power"){
-      Q <- apply(fit$mcmc_result$PARM_samples,1,cont_power_f, d=test_doses)
+      Q <- apply(fit$mcmc_result$PARM_samples,1,.cont_power_f, d=test_doses)
 
     }
     if (fit$model=="FUNL"){
-      Q <- apply(fit$mcmc_result$PARM_samples,1,cont_FUNL_f, d=test_doses)
+      Q <- apply(fit$mcmc_result$PARM_samples,1,.cont_FUNL_f, d=test_doses)
 
     }