Iron out documentation and clear CRAN check; increment version

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mxmmod
 Type: Package
 Title: Measurement Model of Derivatives (MMOD) in OpenMx
-Version: 0.1.0
+Version: 1.0.0
 Authors@R: c(
     person(
       given = 'Kyle',
@@ -16,15 +16,17 @@ Authors@R: c(
     )
   )
 Description: This package provides a convenient interface for building
-    Measurement Model of Derivatives (MMOD; Estabrook, 2015) in OpenMx
-License: Apache 2.0
+    Measurement Model of Derivatives (MMOD; Estabrook, 2015) in OpenMx.
+License: file LICENSE
 Encoding: UTF-8
 LazyData: true
 Imports:
   OpenMx
 Suggests: 
     knitr,
     rmarkdown,
-    testthat
+    testthat,
+    tidyverse
 VignetteBuilder: knitr
 RoxygenNote: 6.1.1
+Depends: R (>= 2.10)

NAMESPACE

@@ -1 +1,3 @@
-exportPattern("^[[:alpha:]]+")
+# Generated by roxygen2: do not edit by hand
+
+export(mxMmodModel)

R/data.R

@@ -0,0 +1,19 @@
+#' NLSY97 Longitudinal Depression Scale Data
+#'
+#' A 5-item depression scale used on the National Longitudinal
+#' Survey of Youth, 1997 sample (NLSY97). Individuals were assessed
+#' in 2000, 2002, and 2004. All items are assessed on a 4-point
+#' likert scale.
+#'
+#' @format A data frame with 26952 rows and 7 variables:
+#' \describe{
+#'   \item{pid}{Unique ID of participant}
+#'   \item{occasion}{Measurement occasion}
+#'   \item{nervous}{How often participant felt 'like a nervous person'}
+#'   \item{calm}{How often participant felt 'calm and peaceful'}
+#'   \item{down}{How often participant felt 'down or blue'}
+#'   \item{happy}{How often participant felt 'like a happy person'}
+#'   \item{depressed}{How often participant felt 'depressed'}
+#' }
+#' @source \url{https://www.bls.gov/nls/nlsy97.htm}
+"nlsy97depression"

R/mxMmodModel.R

@@ -24,16 +24,17 @@
 #' \code{list(F1 = c('m1', 'm2', 'm3'), F2 = c('m4', 'm5', 'm6'))}
 #'
 #' @examples
-#' data("nlsy97depression")
+#' data(nlsy97depression)
 #' # Fit one factor MMOD
 #' structure <- list(
 #'   F1 = c('nervous', 'down', 'depressed', 'calm', 'happy')
 #' )
 #' mmod_model <- mxMmodModel(data=nlsy97depression,
 #'                           modelName='1 Factor MMOD',
 #'                           idvar='pid', timevar='occasion', structure=structure)
-#' mmod_fit <- mxRun(mmod_model)
+#' mmod_fit <- OpenMx::mxRun(mmod_model)
 #' summary(mmod_fit)
+#' @export
 
 mxMmodModel <- function(data, modelName, idvar, timevar, structure, fiml=F) {
   derivName <- function(o, m) {paste0('d', m, '_', o)} # derivName(1, 'nervous') -> dnervous_1
@@ -107,53 +108,60 @@ mxMmodModel <- function(data, modelName, idvar, timevar, structure, fiml=F) {
   manifests <- unique(unlist(derivStruct))
 
   data <- data[c(idvar, timevar, unlist(structure))]
-  data <- reshape(as.data.frame(data), timevar=timevar, idvar=idvar, direction='wide', sep='_')[-1]
+  data <- stats::reshape(as.data.frame(data), timevar=timevar, idvar=idvar, direction='wide', sep='_')[-1]
   stopifnot(setequal(manifests, names(data))) # Sanity check
 
   # OpenMx manifest ordering bug: https://github.com/OpenMx/OpenMx/issues/247
   data <- data[manifests]
 
   if (fiml) {
-    mxd <- mxData(data, type="raw")
+    mxd <- OpenMx::mxData(data, type="raw")
   } else {
     if (any(is.na(data))) {
       warning('Missing values detected; omitting them.')
     }
-    df_subset <- na.omit(data)
-    df_cov <- cov(df_subset)
-    mxd <- mxData(df_cov, type="cov", numObs=nrow(df_subset))
+    df_subset <- stats::na.omit(data)
+    df_cov <- stats::cov(df_subset)
+    mxd <- OpenMx::mxData(df_cov, type="cov", numObs=nrow(df_subset))
   }
 
   # Make weight matrix with Deboeck’s functions
   weight <- ContrastsGOLD(occasions_num, length(occasions_num) - 1)
   weightList <- as.list(as.data.frame(t(weight)))
 
-  do.call('mxModel', c(list(
+  do.call(OpenMx::mxModel, c(list(
     modelName, mxd, type="RAM",
     manifestVars=manifests,
     latentVars=c(factors, derivatives),
     # factor loadings
     mapply(function(fct, drv) {
-      mxPath(from=fct, to=drv, values=0.5, free=T)
+      OpenMx::mxPath(from=fct, to=drv, values=0.5, free=T)
     }, names(factorStruct), factorStruct),
     # factor variances
-    mxPath(from=factors, arrows=2, values=1, free=F),
+    OpenMx::mxPath(from=factors, arrows=2, values=1, free=F),
     # factor correlations
-    mxPath(from=factors, arrows=2, connect="unique.bivariate", free=T),
+    OpenMx::mxPath(from=factors, arrows=2, connect="unique.bivariate", free=T),
     # residual variances(only for latent derivatives !)
-    mxPath(from=derivatives, arrows=2, values=1)),
+    OpenMx::mxPath(from=derivatives, arrows=2, values=1)),
     # transformation
     mapply(function(dgrp, weight) {
        mapply(function(drv, mnf) {
-        mxPath(from=drv, to=mnf, free=F, values=weight)
+        OpenMx::mxPath(from=drv, to=mnf, free=F, values=weight)
       }, names(dgrp), dgrp)
     }, derivStruct, weightList),
     # saturate model
-    if (fiml) mxPath(from = 'one', to = manifests) else list()
+    if (fiml) OpenMx::mxPath(from = 'one', to = manifests) else list()
   ))
 }
 
-# Code from Deboeck (2010)
+#' Generate GOLD contrasts
+#'
+#' Code from Deboeck (2010)
+#'
+#' @param T timeseries
+#' @param max max derivatives
+#'
+#' @keywords internal
 ContrastsGOLD <- function(T, max) {
   Xi <- matrix(NA, length(T), length(T))
   for(r in 0:(length(T)-1)) {

data-raw/nlsy97depression.R

@@ -1,4 +1,7 @@
+# Create nlsy97 depression data set
+#
 # Public domain data from nlsy97: https://www.bls.gov/nls/nlsy97.htm
+#
 
 var_list <- list(
   pid = 'R0000100',

tests/testthat/test-refimpl.R

@@ -1,6 +1,7 @@
 context('Reference implementation comparison')
 
 mxmmod_ref <- function(df, do_fiml=F) {
+  require(OpenMx)
   require(mxmmod)
   structure <- list(
     F1 = c('nervous', 'down', 'depressed', 'calm', 'happy')

vignettes/mmod_tutorial.Rmd

@@ -7,6 +7,7 @@ vignette: >
   %\VignetteIndexEntry{Getting Started with mxmmod}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
+  %\VignetteDepends{tidyverse}
 ---
 
 ```{r setup, include = FALSE}
@@ -35,13 +36,7 @@ E. Discussion
 
 ### Prelim: Prepare environment
 
-Before we begin, install the `mxmmod` package if you have not already:
-
-```{r, eval=F}
-devtools::install_github('khusmann/mxmmod')
-```
-
-Now let's load the required libraries for this tutorial:
+First, let's load the required libraries for this tutorial:
 
 ```{r, message=F, warning=F}
 library(tidyverse)