Showing 19 of total 19 results (show query)
thomasp85
lime:Local Interpretable Model-Agnostic Explanations
When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <arXiv:1602.04938>.
Maintained by Emil Hvitfeldt. Last updated 3 years ago.
caretmodel-checkingmodel-evaluationmodelingcpp
69.6 match 485 stars 11.07 score 732 scripts 1 dependents
pik-piam
limes:The liMES R package
Contains the LIMES-specific routines for data and model output manipulation.
Maintained by Sebastian Osorio. Last updated 9 days ago.
57.6 match 1 stars 4.60 score 5 scripts
modeloriented
live:Local Interpretable (Model-Agnostic) Visual Explanations
Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>. More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.
Maintained by Mateusz Staniak. Last updated 6 years ago.
imlinterpretabilitylimemachine-learningmodel-visualizationvisual-explanationsxai
16.2 match 35 stars 5.59 score 55 scriptsmodeloriented
localModel:LIME-Based Explanations with Interpretable Inputs Based on Ceteris Paribus Profiles
Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable inputs are created based on local rather than global behaviour of each original feature.
Maintained by Przemyslaw Biecek. Last updated 3 years ago.
10.1 match 14 stars 6.16 score 23 scripts
insightsengineering
sasr:'SAS' Interface
Provides a 'SAS' interface, through 'SASPy'(<https://sassoftware.github.io/saspy/>) and 'reticulate'(<https://rstudio.github.io/reticulate/>). This package helps you create 'SAS' sessions, execute 'SAS' code in remote 'SAS' servers, retrieve execution results and log, and exchange datasets between 'SAS' and 'R'. It also helps you to install 'SASPy' and create a configuration file for the connection. Please review the 'SASPy' license file as instructed so that you comply with its separate and independent license.
Maintained by Liming Li. Last updated 2 years ago.
9.1 match 15 stars 5.90 score 21 scripts
bips-hb
innsight:Get the Insights of Your Neural Network
Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' or 'Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).
Maintained by Niklas Koenen. Last updated 4 months ago.
5.1 match 30 stars 7.01 score 57 scripts
openpharma
mmrm:Mixed Models for Repeated Measures
Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.
Maintained by Daniel Sabanes Bove. Last updated 10 days ago.
1.5 match 138 stars 12.15 score 113 scripts 4 dependents
pharmaverse
admiralonco:Oncology Extension Package for ADaM in 'R' Asset Library
Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the 'admiral' package.
Maintained by Stefan Bundfuss. Last updated 2 months ago.
1.5 match 32 stars 8.66 score 30 scriptsinsightsengineering
chevron:Standard TLGs for Clinical Trials Reporting
Provide standard tables, listings, and graphs (TLGs) libraries used in clinical trials. This package implements a structure to reformat the data with 'dunlin', create reporting tables using 'rtables' and 'tern' with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality.
Maintained by Joe Zhu. Last updated 24 days ago.
clinical-trialsgraphslistingsnestreportingtables
1.5 match 12 stars 8.24 score 12 scriptsmodeloriented
triplot:Explaining Correlated Features in Machine Learning Models
Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <arXiv:1806.08915>.
Maintained by Katarzyna Pekala. Last updated 4 years ago.
explanationsexplanatory-model-analysismachine-learningmodel-visualizationxai
3.3 match 9 stars 3.65 score 7 scripts
katilingban
paleta:Collection of Palettes, Themes, and Theme Components
A collection of palettes, themes, and theme components based on publicly available branding guidelines of various non-governmental organisations, government agencies, and United Nations units.
Maintained by Ernest Guevarra. Last updated 2 months ago.
2.7 match 2 stars 4.48 score 8 scriptsinsightsengineering
dunlin:Preprocessing Tools for Clinical Trial Data
A collection of functions to preprocess data and organize them in a format amenable to use by chevron.
Maintained by Joe Zhu. Last updated 24 days ago.
1.5 match 4 stars 7.38 score 30 scripts 1 dependents
jamgreen
lehdr:Grab Longitudinal Employer-Household Dynamics (LEHD) Flat Files
Designed to query Longitudinal Employer-Household Dynamics (LEHD) workplace/residential association and origin-destination flat files and optionally aggregate Census block-level data to block group, tract, county, or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES8/>.
Maintained by Jamaal Green. Last updated 4 months ago.
1.6 match 62 stars 7.05 score 90 scripts
peterkdunn
GLMsData:Generalized Linear Model Data Sets
Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
Maintained by Peter K. Dunn. Last updated 3 years ago.
3.8 match 2.61 score 220 scriptsinsightsengineering
autoslider.core:Slide Automation for Tables, Listings and Figures
The normal process of creating clinical study slides is that a statistician manually type in the numbers from outputs and a separate statistician to double check the typed in numbers. This process is time consuming, resource intensive, and error prone. Automatic slide generation is a solution to address these issues. It reduces the amount of work and the required time when creating slides, and reduces the risk of errors from manually typing or copying numbers from the output to slides. It also helps users to avoid unnecessary stress when creating large amounts of slide decks in a short time window.
Maintained by Joe Zhu. Last updated 2 months ago.
1.5 match 3 stars 5.91 score 3 scriptsinsightsengineering
teal.osprey:'teal' Modules for TLG Functions in Osprey
Community efforts to collect 'teal' modules for TLGs defined in 'osprey' package. Enables 'teal' app developers to create 'shiny' applications with a use of 'osprey' analysis functions.
Maintained by Nina Qi. Last updated 19 days ago.
1.5 match 5 stars 5.12 score 1 scripts
giuseppec
iml:Interpretable Machine Learning
Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley (2018) <doi:10.48550/arxiv.1612.08468>, partial dependence plots described by Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime') described by Ribeiro et. al (2016) <doi:10.48550/arXiv.1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Maintained by Giuseppe Casalicchio. Last updated 20 days ago.
0.5 match 494 stars 12.86 score 642 scripts 4 dependents
sunsmiling
PPtreeregViz:Projection Pursuit Regression Tree Visualization
It was developed as a tool for exploring 'PPTreereg' (Projection Pursuit TREE of REGression). It uses various projection pursuit indexes and 'XAI' (eXplainable Artificial Intelligence) methods to help understand the model by finding connections between the input variables and prediction values of the model. The 'KernelSHAP' (Aas, Jullum and Løland (2019) <arXiv:1903.10464>) algorithm was modified to fit ‘PPTreereg’, and some codes were modified from the 'shapr' package (Sellereite, Nikolai, and Martin Jullum (2020) <doi:10.21105/joss.02027>). The implemented methods help to explore the model at the single instance level as well as at the whole dataset level. Users can compare with other machine learning models by applying it to the 'DALEX' package of 'R'.
Maintained by HyunSun Cho. Last updated 1 years ago.
1.7 match 2 stars 3.00 score 3 scripts