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Found 22 packages in 0.01 seconds

breakDown — by Przemyslaw Biecek, 3 months ago

Model Agnostic Explainers for Individual Predictions

Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models.

randomForestExplainer — by Aleksandra Paluszynska, a year ago

Explaining and Visualizing Random Forests in Terms of Variable Importance

A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) , Leo Breiman (2001) ).

sejmRP — by Piotr Smuda, a year ago

An Information About Deputies and Votings in Polish Diet from Seventh to Eighth Term of Office

Set of functions that access information about deputies and votings in Polish diet from webpage < http://www.sejm.gov.pl>. The package was developed as a result of an internship in MI2 Group - < http://mi2.mini.pw.edu.pl>, Faculty of Mathematics and Information Science, Warsaw University of Technology.

archivist.github — by Marcin Kosinski, 2 months ago

Tools for Archiving, Managing and Sharing R Objects via GitHub

The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package.

coxphSGD — by Marcin Kosinski, a year ago

Stochastic Gradient Descent log-Likelihood Estimation in Cox Proportional Hazards Model

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

cr17 — by Magda MĹ‚ynarczyk, a year ago

Testing Differences Between Competing Risks Models and Their Visualisations

Tool for analyzing competing risks models. The main point of interest is testing differences between groups (as described in R.J Gray (1988) and J.P. Fine, R.J Gray (1999) ) and visualizations of survival and cumulative incidence curves.

auditor — by Alicja Gosiewska, 6 days ago

Model Audit - Verification, Validation, and Error Analysis

Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.

intsvy — by Daniel Caro, 4 days ago

International Assessment Data Manager

Provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA. ICILS, and PIAAC).

factorMerger — by Agnieszka Sitko, 6 months ago

The Merging Path Plot

The Merging Path Plot is a methodology for adaptive fusing of k-groups with likelihood-based model selection. This package contains tools for exploration and visualization of k-group dissimilarities. Comparison of k-groups is one of the most important issues in exploratory analyses and it has zillions of applications. The traditional approach is to use pairwise post hoc tests in order to verify which groups differ significantly. However, this approach fails with a large number of groups in both interpretation and visualization layer. The Merging Path Plot solves this problem by using an easy-to-understand description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic. Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.

survxai — by Aleksandra Grudziaz, a month ago

Visualization of the Local and Global Survival Model Explanations

Survival models may have very different structures. This package contains functions for creating a unified representation of a survival models, which can be further processed by various survival explainers. Tools implemented in 'survxai' help to understand how input variables are used in the model and what impact do they have on the final model prediction. Currently, four explanation methods are implemented. We can divide them into two groups: local and global.