version 1.0.2

DESCRIPTION

@@ -1,11 +1,10 @@
 Package: EHRtemporalVariability
 Type: Package
-Title: Delineating Reference Changes in Electronic Health Records over
-        Time
-Version: 1.0.1
-Date: 2019-05-17
+Title: Delineating Temporal Dataset Shifts in Electronic Health Records
+Version: 1.0.2
+Date: 2019-09-26
 Encoding: UTF-8
-Description: The 'EHRtemporalVariability' package contains functions to delineate reference changes over time in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds uncovering the patterns of the data latent temporal variability. Results can be explored through visual analytics formats such as Data Temporal Heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding.
+Description: The 'EHRtemporalVariability' package contains functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds uncovering the patterns of the data latent temporal variability. Dataset shifts can be explored and identified through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding.
 Depends: R (>= 3.3.0), dplyr
 License: Apache License 2.0 | file LICENSE
 LazyData: true
@@ -26,7 +25,7 @@ Authors@R: c(
 Maintainer: Carlos Sáez <carsaesi@upv.es>
 RoxygenNote: 6.1.1
 URL: http://github.com/hms-dbmi/EHRtemporalVariability
-Packaged: 2019-06-11 14:08:27 UTC; Carlos
+Packaged: 2019-09-26 10:01:55 UTC; Carlos
 Author: Carlos Sáez [aut, cre],
   Alba Gutiérrez-Sacristán [aut],
   Isaac Kohane [aut],
@@ -36,4 +35,4 @@ Author: Carlos Sáez [aut, cre],
     (Spain) [cph],
   Department of Biomedical Informatics, Harvard Medical School [cph]
 Repository: CRAN
-Date/Publication: 2019-06-11 16:20:17 UTC
+Date/Publication: 2019-09-26 15:30:06 UTC

MD5

@@ -1,7 +1,7 @@
-eb7680796c7155a5ab26bc857b6305a1 *DESCRIPTION
+3d7b6c69c5035d4bf35341135c2f7fc8 *DESCRIPTION
 3d113347136ff8800e34bb85c10182e0 *LICENSE
 3a4316b07f2fcd748f7e7c23e87d9c3f *NAMESPACE
-25ef80ee7b01db805871ba6b795cb6c3 *NEWS.md
+eda4387d82ceb9bd2623fafd4cf39524 *NEWS.md
 98f80368e091e8c135c6463adad8fb5f *R/allClasses.R
 b8c49163f882ed50ae6be1a8cfdd4768 *R/allGenerics.R
 ce436105aba109cd2bff7b685340ba2f *R/allMethods.R
@@ -10,11 +10,11 @@ adcd49e3dfc6bdd1dee8912b420b7a0a *R/estimateDataTemporalMap.R
 bb608d8e9632ce8e45da18b9f3326ee8 *R/icd9toPheWAS.R
 4a959de0ce8ed9b32f365335980f9a47 *R/igtProjectionCore.R
 64064c10a01b033a82744026d135ab13 *R/jsdiv.R
-873f7e2aef9e13f9b50a2faebde0b011 *README.md
-85a718ce0c78b6320bfc0b50c09582b4 *build/vignette.rds
+e26106f0fee4b5411bfaf2428c8c71e8 *README.md
+3458067b3e3ec41125109e8689f884e7 *build/vignette.rds
 b18a1ccef88df959fdd2068ffef62f29 *inst/doc/EHRtemporalVariability.R
-4d925c76bf4b401ab9fc9d85533dcf62 *inst/doc/EHRtemporalVariability.Rmd
-cab02408cf2542df01683383266f8f3b *inst/doc/EHRtemporalVariability.pdf
+a0cfc122478abe480ca0306615d157d7 *inst/doc/EHRtemporalVariability.Rmd
+cfc62f77c659e5ecac569933d927c94a *inst/doc/EHRtemporalVariability.html
 18b5b9c9c3bf7d09a494428d7144d146 *inst/extdata/icd9mappingFile.csv
 4dc188b27fb4eccefcd063b67f4a4982 *inst/extdata/nhdsSubset.csv
 0d1727c2ac4faf1c457049b30a0a77ef *inst/extdata/variabilityDemoNHDSdiagcode1-phewascode.RData
@@ -26,9 +26,9 @@ c5e187fa25188ea38c908471ad7c62f8 *man/DataTemporalMap-class.Rd
 7ec3a8571640cf71cc471c0dce314107 *man/plotDataTemporalMap-methods.Rd
 53d0f093e1435634e81380a3ed3f2e29 *man/plotIGTProjection-methods.Rd
 30ac8d74c2c5eeb8c3873148f650ab50 *man/trimDataTemporalMap-methods.Rd
-4d925c76bf4b401ab9fc9d85533dcf62 *vignettes/EHRtemporalVariability.Rmd
-34c3fe11d3d500394632faff7414866c *vignettes/EHRtemporalVariability.html
+a0cfc122478abe480ca0306615d157d7 *vignettes/EHRtemporalVariability.Rmd
+a84c82b3f7a01abb229c24287a7fec52 *vignettes/EHRtemporalVariabilityHelp.pdf
 1ed61e48f7830da361fa0f2323b07926 *vignettes/biomed-central.csl
 2af454a5e1caae5bb44a4e6301bf9048 *vignettes/createAutomaticPDFfunctions.R
-32ecddca4edf8540b70b582bdd32377c *vignettes/general-overview.bib
+ad412317200c09a8014b7cfa36678f47 *vignettes/general-overview.bib
 8e3ed11227f5f8b2916ec4bc0ac023f9 *vignettes/rpackageworkflow.png

NEWS.md

@@ -1,3 +1,7 @@
+# EHRtemporalVariability 1.0.2
+
+* Description texts, references and vignette have been fixed and updated.
+
 # EHRtemporalVariability 1.0.1
 
 ## Bug fixes

README.md

@@ -1,30 +1,30 @@
-# EHRtemporalVariability [![](http://www.r-pkg.org/badges/version/EHRtemporalVariability)](https://cran.r-project.org/package=EHRtemporalVariability)
+# EHRtemporalVariability [![](http://www.r-pkg.org/badges/version/EHRtemporalVariability)](https://cran.r-project.org/package=EHRtemporalVariability) [![](http://cranlogs.r-pkg.org/badges/grand-total/EHRtemporalVariability)](http://cranlogs.r-pkg.org/badges/grand-total/EHRtemporalVariability)
 
-`EHRtemporalVariability` is an R package for delineating reference changes in Eletronic Health Records over time
+`EHRtemporalVariability` is an R package for delineating temporal dataset shifts in Eletronic Health Records
 
 ## What is this repository for?
 
-The `EHRtemporalVariability` package contains functions to delineate reference changes over time in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds uncovering the patterns of the data latent temporal variability. Results can be explored through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots [1-3](https://github.com/hms-dbmi/EHRtemporalVariability#Citation). An additional [EHRtemporalVariability Shiny app](https://github.com/hms-dbmi/EHRtemporalVariability-shiny) can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding.
+The `EHRtemporalVariability` package contains functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. Dataset shifts can be explored and identified through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots [1-3](https://github.com/hms-dbmi/EHRtemporalVariability#Citation). An additional [EHRtemporalVariability Shiny app](https://github.com/hms-dbmi/EHRtemporalVariability-shiny) can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding.
 
 ## Package' Status
 
- * __Version__: 1.01
+ * __Version__: 1.0.2
  * __Authors__: Carlos Sáez (UPV-HMS), Alba Gutiérrez-Sacristán (HMS), Isaac Kohane (HMS), Juan M García-Gómez (UPV), Paul Avillach (HMS)
  * __Maintainer__: Carlos Sáez (UPV-HMS)
 
  Copyright: 2019 - Biomedical Data Science Lab, Universitat Politècnica de València, Spain (UPV) - Department of Biomedical Informatics, Harvard Medical School (HMS)
 
 ## Documentation
 
-* Vignette: [EHRtemporalVariability: Delineating reference changes in Eletronic Health Records over time](http://personales.upv.es/carsaesi/EHRtemporalVariability.html)
+* Vignette: [EHRtemporalVariability: Delineating temporal dataset shifts in electronic health records](http://personales.upv.es/carsaesi/EHRtemporalVariability.html)
 
-* [Package help](https://github.com/hms-dbmi/EHRtemporalVariability/raw/master/vignettes/EHRtemporalVariability.pdf)
+* [Package help](https://github.com/hms-dbmi/EHRtemporalVariability/raw/master/vignettes/EHRtemporalVariabilityHelp.pdf)
 
 ## Citation
 
 A paper describing the EHRtemporalVariability package has been submitted. If you use EHRtemporalVariability, please cite:
 
-Sáez C, Gutiérrez-Sacristán A, Isaac Kohane, García-Gómez JM, Avillach P. EHRtemporalVariability: delineating unknown reference changes in Electronic Health Records over time. (Submitted)
+Sáez C, Gutiérrez-Sacristán A, Isaac Kohane, García-Gómez JM, Avillach P. EHRtemporalVariability: delineating temporal dataset shifts in electronic health records. (Submitted)
 
 The original methods and case studies using the approach are described here:
 

inst/doc/EHRtemporalVariability.Rmd

@@ -1,6 +1,6 @@
 ---
-title: "EHRtemporalVariability: Delineating reference changes in Eletronic Health Records over time"
-date: "June 7, 2019"
+title: "EHRtemporalVariability: Delineating temporal dataset shifts in Electronic Health Records"
+date: "September 15, 2019"
 package: "EHRtemporalVariability `r packageVersion('EHRtemporalVariability')`"
 author:
 - name: Carlos Sáez
@@ -36,10 +36,10 @@ knitr::opts_chunk$set(echo = TRUE)
 ```
 
 # Introduction
-The `EHRtemporalVariability` package contains functions to delineate reference changes over time in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds uncovering the patterns of the data latent temporal variability. Results can be explored through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots [@saez_probabilistic_2015,@saez2016applying]. An additional [EHRtemporalVariability Shiny app](https://github.com/hms-dbmi/variability-shiny) can be used to load and explore the package results towards an improved investigation experience and even to allow the use of these functions to those users non-experienced in R coding.
+The `EHRtemporalVariability` package contains functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. Dataset shifts can be explored and identified through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots [@saez_probabilistic_2015; @saez2016applying; @saez2018kinematics]. An additional [EHRtemporalVariability Shiny app](https://github.com/hms-dbmi/variability-shiny) can be used to load and explore the package results towards an improved investigation experience and even to allow the use of these functions to those users non-experienced in R coding.
 
 ## Background
-Biomedical Data research repositories and property biomedical research databases are becoming bigger both in terms of sample size and collected variables [@Gewin2016,@Andreu-Perez2015]. Two significant reasons behind of this are the widespread adoption of data-sharing initiatives and technological infrastructures, and the continuous and systematic population of those repositories over longer periods of time. However, these two situations can also introduce potential confounding factors in data which may hinder their reuse for research, such as in population research or in statistical and machine learning modelling. Concretely, differences in protocols, populations, or even unexpected biases, either caused by systems or humans, can lead to changes of reference in data over time. This data temporal variability will be reflected on its statistical distributions, related to the above mentioned confounding factors which, in the end, represent a Data Quality (DQ) issue which must be addressed for a reliable data reuse [@saez2016applying,@schlegel2017secondary].
+Biomedical Data research repositories and property biomedical research databases are becoming bigger both in terms of sample size and collected variables [@Gewin2016; @Andreu-Perez2015]. Two significant reasons behind of this are the widespread adoption of data-sharing initiatives and technological infrastructures, and the continuous and systematic population of those repositories over longer periods of time. However, these two situations can also introduce potential confounding factors in data which may hinder their reuse for research, such as in population research or in statistical and machine learning modeling. Concretely, differences in protocols, populations, or even unexpected biases, either caused by systems or humans, can lead to temporal dataset shifts [@quionero2009dataset; @moreno2012unifying], changes of reference which are reflected in the statistical distributions of data. This temporal variability of data represent a Data Quality (DQ) issue which must be addressed for a reliable data reuse [@saez2016applying; @schlegel2017secondary].
 
 The `EHRtemporalVariability` package has been developed to help preventing this problem.
 
@@ -135,7 +135,6 @@ dataset <- read.csv2( "http://github.com/hms-dbmi/EHRtemporalVariability-DataExa
 head( dataset)
 ```
 
-
 ## Transform the date column in 'Date' R format
 The second step will be transform the date column in 'Date' R format. The `EHRtemporalVariability` R package allows the user to do this transformation applying the `formatDate` function.
 

vignettes/EHRtemporalVariability.Rmd

@@ -1,6 +1,6 @@
 ---
-title: "EHRtemporalVariability: Delineating reference changes in Eletronic Health Records over time"
-date: "June 7, 2019"
+title: "EHRtemporalVariability: Delineating temporal dataset shifts in Electronic Health Records"
+date: "September 15, 2019"
 package: "EHRtemporalVariability `r packageVersion('EHRtemporalVariability')`"
 author:
 - name: Carlos Sáez
@@ -36,10 +36,10 @@ knitr::opts_chunk$set(echo = TRUE)
 ```
 
 # Introduction
-The `EHRtemporalVariability` package contains functions to delineate reference changes over time in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds uncovering the patterns of the data latent temporal variability. Results can be explored through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots [@saez_probabilistic_2015,@saez2016applying]. An additional [EHRtemporalVariability Shiny app](https://github.com/hms-dbmi/variability-shiny) can be used to load and explore the package results towards an improved investigation experience and even to allow the use of these functions to those users non-experienced in R coding.
+The `EHRtemporalVariability` package contains functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. Dataset shifts can be explored and identified through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots [@saez_probabilistic_2015; @saez2016applying; @saez2018kinematics]. An additional [EHRtemporalVariability Shiny app](https://github.com/hms-dbmi/variability-shiny) can be used to load and explore the package results towards an improved investigation experience and even to allow the use of these functions to those users non-experienced in R coding.
 
 ## Background
-Biomedical Data research repositories and property biomedical research databases are becoming bigger both in terms of sample size and collected variables [@Gewin2016,@Andreu-Perez2015]. Two significant reasons behind of this are the widespread adoption of data-sharing initiatives and technological infrastructures, and the continuous and systematic population of those repositories over longer periods of time. However, these two situations can also introduce potential confounding factors in data which may hinder their reuse for research, such as in population research or in statistical and machine learning modelling. Concretely, differences in protocols, populations, or even unexpected biases, either caused by systems or humans, can lead to changes of reference in data over time. This data temporal variability will be reflected on its statistical distributions, related to the above mentioned confounding factors which, in the end, represent a Data Quality (DQ) issue which must be addressed for a reliable data reuse [@saez2016applying,@schlegel2017secondary].
+Biomedical Data research repositories and property biomedical research databases are becoming bigger both in terms of sample size and collected variables [@Gewin2016; @Andreu-Perez2015]. Two significant reasons behind of this are the widespread adoption of data-sharing initiatives and technological infrastructures, and the continuous and systematic population of those repositories over longer periods of time. However, these two situations can also introduce potential confounding factors in data which may hinder their reuse for research, such as in population research or in statistical and machine learning modeling. Concretely, differences in protocols, populations, or even unexpected biases, either caused by systems or humans, can lead to temporal dataset shifts [@quionero2009dataset; @moreno2012unifying], changes of reference which are reflected in the statistical distributions of data. This temporal variability of data represent a Data Quality (DQ) issue which must be addressed for a reliable data reuse [@saez2016applying; @schlegel2017secondary].
 
 The `EHRtemporalVariability` package has been developed to help preventing this problem.
 
@@ -135,7 +135,6 @@ dataset <- read.csv2( "http://github.com/hms-dbmi/EHRtemporalVariability-DataExa
 head( dataset)
 ```
 
-
 ## Transform the date column in 'Date' R format
 The second step will be transform the date column in 'Date' R format. The `EHRtemporalVariability` R package allows the user to do this transformation applying the `formatDate` function.