madshapR - Support Technical Processes Following 'Maelstrom Research' Standards

Functions to support rigorous processes in data cleaning, evaluation, and documentation across datasets from different studies based on Maelstrom Research guidelines. The package includes the core functions to evaluate and format the main inputs that define the process, diagnose errors, and summarize and evaluate datasets and their associated data dictionaries. The main outputs are clean datasets and associated metadata, and tabular and visual summary reports. As described in Maelstrom Research guidelines for rigorous retrospective data harmonization (Fortier I and al. (2017) <doi:10.1093/ije/dyw075>).

Last updated 9 months ago

5.40 score 2 stars 3 dependents 28 scripts 237 downloads

Rmonize - Support Retrospective Harmonization of Data

Functions to support rigorous retrospective data harmonization processing, evaluation, and documentation across datasets from different studies based on Maelstrom Research guidelines. The package includes the core functions to evaluate and format the main inputs that define the harmonization process, apply specified processing rules to generate harmonized data, diagnose processing errors, and summarize and evaluate harmonized outputs. The main inputs that define the processing are a DataSchema (list and definitions of harmonized variables to be generated) and Data Processing Elements (processing rules to be applied to generate harmonized variables from study-specific variables). The main outputs of processing are harmonized datasets, associated metadata, and tabular and visual summary reports. As described in Maelstrom Research guidelines for rigorous retrospective data harmonization (Fortier I and al. (2017) <doi:10.1093/ije/dyw075>).

Last updated 10 months ago

5.20 score 5 stars 21 scripts 171 downloads

fabR - Wrapper Functions Collection Used in Data Pipelines

The goal of this package is to provide wrapper functions in the data cleaning and cleansing processes. These function helps in messages and interaction with the user, keep track of information in pipelines, help in the wrangling, munging, assessment and visualization of data frame-like material.

Last updated 9 months ago

5.14 score 1 stars 4 dependents 23 scripts 361 downloads

banffIT - Automatize Diagnosis Standardized Assignation Using the Banff Classification

Assigns standardized diagnoses using the Banff Classification (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell mediated rejection as well as Active, Chronic active, and Chronic antibody mediated rejection). The main function considers a minimal dataset containing biopsies information in a specific format (described by a data dictionary), verifies its content and format (based on the data dictionary), assigns diagnoses, and creates a summary report. The package is developed on the reference guide to the Banff classification of renal allograft pathology Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) <doi:10.1097/TP.0000000000002366>. The full description of the Banff classification is available at <https://banfffoundation.org/>.

Last updated 9 months ago

4.00 score 539 downloads

mlstrOpalr - Support Compatibility Between 'Maelstrom' R Packages and 'Opal' Environment

Functions to support compatibility between 'Maelstrom' R packages and 'Opal' environment. 'Opal' is the 'OBiBa' core database application for biobanks. It is used to build data repositories that integrates data collected from multiple sources. 'Opal Maelstrom' is a specific implementation of this software. This 'Opal' client is specifically designed to interact with 'Opal Maelstrom' distributions to perform operations on the R server side. The user must have adequate credentials. Please see <https://opaldoc.obiba.org/> for complete documentation.

Last updated 9 months ago

3.70 score 1 stars 3 scripts 163 downloads