Showing 120 of total 120 results (show query)
bxc147
Epi:Statistical Analysis in Epidemiology
Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.
Maintained by Bendix Carstensen. Last updated 2 months ago.
17.1 match 4 stars 9.65 score 708 scripts 11 dependents
fbertran
SelectBoost:A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
Maintained by Frederic Bertrand. Last updated 2 years ago.
confidencecorrelationcorrelation-structuremodellingprecisionrecallselection-algorithm
28.4 match 7 stars 5.61 score 13 scripts 1 dependents
feakster
QDiabetes:Type 2 Diabetes Risk Calculator
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
Maintained by Benjamin G. Feakins. Last updated 4 years ago.
clinriskdiabetesdiabetes-predictiondiabetes-riskdiabetes-risk-predictionprognosticqdiabetes-algorithmqtoolsrisk
37.0 match 7 stars 3.85 score 5 scripts
julianfaraway
faraway:Datasets and Functions for Books by Julian Faraway
Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).
Maintained by Julian Faraway. Last updated 1 months ago.
10.9 match 29 stars 9.43 score 1.7k scripts 1 dependents
lightbluetitan
MedDataSets:Comprehensive Medical, Disease, Treatment, and Drug Datasets
Provides an extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health. This package covers topics such as drug effectiveness, vaccine trials, survival rates, infectious disease outbreaks, and medical treatments. The included datasets span various health conditions, including AIDS, cancer, bacterial infections, and COVID-19, along with information on pharmaceuticals and vaccines. These datasets are sourced from the R ecosystem and other R packages, remaining unaltered to ensure data integrity. This package serves as a valuable resource for researchers, analysts, and healthcare professionals interested in conducting medical and public health data analysis in R.
Maintained by Renzo Caceres Rossi. Last updated 5 months ago.
18.1 match 8 stars 5.68 score 60 scripts
therneau
survival:Survival Analysis
Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.
Maintained by Terry M Therneau. Last updated 3 months ago.
4.5 match 400 stars 20.43 score 29k scripts 3.9k dependents
cran
gss:General Smoothing Splines
A comprehensive package for structural multivariate function estimation using smoothing splines.
Maintained by Chong Gu. Last updated 5 months ago.
12.5 match 3 stars 6.40 score 137 dependentscran
MASS:Support Functions and Datasets for Venables and Ripley's MASS
Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).
Maintained by Brian Ripley. Last updated 17 days ago.
7.5 match 19 stars 10.53 score 11k dependents
openintrostat
openintro:Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs
Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.
Maintained by Mine Çetinkaya-Rundel. Last updated 3 months ago.
6.8 match 240 stars 11.39 score 6.0k scripts
dmphillippo
multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data
Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.
Maintained by David M. Phillippo. Last updated 3 days ago.
7.3 match 35 stars 9.11 score 163 scripts
irinagain
iglu:Interpreting Glucose Data from Continuous Glucose Monitors
Implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series plots, lasagna plots and ambulatory glucose profile report.
Maintained by Irina Gaynanova. Last updated 11 days ago.
6.8 match 26 stars 9.00 score 39 scripts
statistikat
VIM:Visualization and Imputation of Missing Values
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
Maintained by Matthias Templ. Last updated 7 months ago.
hotdeckimputation-methodsmodel-predictionsvisualizationcpp
3.8 match 85 stars 14.44 score 2.6k scripts 19 dependents
kkholst
mets:Analysis of Multivariate Event Times
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Maintained by Klaus K. Holst. Last updated 3 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
4.0 match 14 stars 13.47 score 236 scripts 42 dependents
friendly
heplots:Visualizing Hypothesis Tests in Multivariate Linear Models
Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Maintained by Michael Friendly. Last updated 9 days ago.
linear-hypothesesmatricesmultivariate-linear-modelsplotrepeated-measure-designsvisualizing-hypothesis-tests
4.5 match 9 stars 11.49 score 1.1k scripts 7 dependents
kurthornik
mlbench:Machine Learning Benchmark Problems
A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
Maintained by Kurt Hornik. Last updated 3 months ago.
5.6 match 2 stars 8.93 score 5.0k scripts 55 dependents
luca-scr
mclust:Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Maintained by Luca Scrucca. Last updated 11 months ago.
4.0 match 21 stars 12.23 score 6.6k scripts 587 dependents
nspyrison
spinifex:Manual Tours, Manual Control of Dynamic Projections of Numeric Multivariate Data
Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The 'spinifex' packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the 'tourr' package Wickham et al. <doi:10.18637/jss.v040.i02>. 'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 'plotly' graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) <doi:10.32614/RJ-2020-027>.
Maintained by Nicholas Spyrison. Last updated 2 months ago.
dimensionreductiontoursvisualization
7.2 match 3 stars 6.28 score 105 scripts 1 dependents
guido-s
netmeta:Network Meta-Analysis using Frequentist Methods
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - subgroup network meta-analysis.
Maintained by Guido Schwarzer. Last updated 2 days ago.
meta-analysisnetwork-meta-analysisrstudio
3.8 match 33 stars 11.82 score 199 scripts 10 dependents
bgreenwell
pdp:Partial Dependence Plots
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
Maintained by Brandon M. Greenwell. Last updated 3 years ago.
black-box-modelmachine-learningpartial-dependence-functionpartial-dependence-plotvisualization
3.8 match 93 stars 11.72 score 1.1k scripts 8 dependents
r-forge
coin:Conditional Inference Procedures in a Permutation Test Framework
Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.
Maintained by Torsten Hothorn. Last updated 9 months ago.
3.8 match 11.68 score 1.6k scripts 74 dependents
rpruim
fastR2:Foundations and Applications of Statistics Using R (2nd Edition)
Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet() function.
Maintained by Randall Pruim. Last updated 1 years ago.
7.2 match 13 stars 5.85 score 108 scripts
scheike
timereg:Flexible Regression Models for Survival Data
Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.
Maintained by Thomas Scheike. Last updated 6 months ago.
4.0 match 31 stars 10.42 score 289 scripts 44 dependents
valentint
rrcov:Scalable Robust Estimators with High Breakdown Point
Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Maintained by Valentin Todorov. Last updated 7 months ago.
3.8 match 2 stars 10.57 score 484 scripts 96 dependents
andyliaw-mrk
locfit:Local Regression, Likelihood and Density Estimation
Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.
Maintained by Andy Liaw. Last updated 12 days ago.
4.0 match 1 stars 9.40 score 428 scripts 606 dependents
tagteam
Publish:Format Output of Various Routines in a Suitable Way for Reports and Publication
A bunch of convenience functions that transform the results of some basic statistical analyses into table format nearly ready for publication. This includes descriptive tables, tables of logistic regression and Cox regression results as well as forest plots.
Maintained by Thomas A. Gerds. Last updated 11 days ago.
3.6 match 15 stars 10.11 score 274 scripts 36 dependents
statsgary
MLDataR:Collection of Machine Learning Datasets for Supervised Machine Learning
Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.
Maintained by Gary Hutson. Last updated 1 years ago.
6.0 match 53 stars 5.70 score 19 scripts
pistacliffcho
icenReg:Regression Models for Interval Censored Data
Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data.
Maintained by Clifford Anderson-Bergman. Last updated 1 years ago.
5.8 match 1 stars 5.74 score 140 scripts 11 dependents
gunhanb
nmaINLA:Network Meta-Analysis using Integrated Nested Laplace Approximations
Performs network meta-analysis using integrated nested Laplace approximations ('INLA') which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis dataset. 'INLA' package can be obtained from <https://www.r-inla.org>.
Maintained by Burak Kuersad Guenhan. Last updated 4 years ago.
6.8 match 7 stars 4.85 score 8 scripts
trevorhastie
lars:Least Angle Regression, Lasso and Forward Stagewise
Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below.
Maintained by Trevor Hastie. Last updated 3 years ago.
4.0 match 6 stars 7.98 score 700 scripts 78 dependents
hugaped
MBNMAdose:Dose-Response MBNMA Models
Fits Bayesian dose-response model-based network meta-analysis (MBNMA) that incorporate multiple doses within an agent by modelling different dose-response functions, as described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. By modelling dose-response relationships this can connect networks of evidence that might otherwise be disconnected, and can improve precision on treatment estimates. Several common dose-response functions are provided; others may be added by the user. Various characteristics and assumptions can be flexibly added to the models, such as shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting at the treatment level.
Maintained by Hugo Pedder. Last updated 1 months ago.
4.4 match 10 stars 6.60 score
robjhyndman
fpp2:Data for "Forecasting: Principles and Practice" (2nd Edition)
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.
Maintained by Rob Hyndman. Last updated 2 years ago.
3.4 match 106 stars 8.57 score 1.8k scripts 1 dependents
bioc
spicyR:Spatial analysis of in situ cytometry data
The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.
Maintained by Ellis Patrick. Last updated 12 days ago.
singlecellcellbasedassaysspatial
3.6 match 9 stars 8.02 score 57 scripts 1 dependents
scottkosty
bootstrap:Functions for the Book "An Introduction to the Bootstrap"
Software (bootstrap, cross-validation, jackknife) and data for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package "boot".
Maintained by Scott Kostyshak. Last updated 6 years ago.
3.6 match 7.62 score 890 scripts 30 dependents
mlr-org
mlr3:Machine Learning in R - Next Generation
Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Maintained by Marc Becker. Last updated 5 days ago.
classificationdata-sciencemachine-learningmlr3regression
1.8 match 972 stars 14.86 score 2.3k scripts 35 dependents
thothorn
exactRankTests:Exact Distributions for Rank and Permutation Tests
Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.
Maintained by Torsten Hothorn. Last updated 3 years ago.
3.8 match 1 stars 7.13 score 276 scripts 65 dependentsfriendly
candisc:Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative.
Maintained by Michael Friendly. Last updated 10 months ago.
dimension-reductionmultivariate-linear-modelsvisualization
3.0 match 15 stars 8.86 score 221 scripts 3 dependents
higgi13425
medicaldata:Data Package for Medical Datasets
Provides access to well-documented medical datasets for teaching. Featuring several from the Teaching of Statistics in the Health Sciences website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical research, some reformatted and extended from existing R packages, and some data donations.
Maintained by Peter Higgins. Last updated 2 years ago.
3.4 match 48 stars 7.43 score 317 scripts
aalfons
ccaPP:(Robust) Canonical Correlation Analysis via Projection Pursuit
Canonical correlation analysis and maximum correlation via projection pursuit, as well as fast implementations of correlation estimators, with a focus on robust and nonparametric methods.
Maintained by Andreas Alfons. Last updated 6 months ago.
4.5 match 2 stars 5.58 score 27 scripts 3 dependents
hoksanyip
SVMMaj:Implementation of the SVM-Maj Algorithm
Implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.
Maintained by Hoksan Yip. Last updated 4 months ago.
7.2 match 1 stars 3.36 score 23 scripts
uniprjrc
fsdaR:Robust Data Analysis Through Monitoring and Dynamic Visualization
Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis (FSDA)' which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis.
Maintained by Valentin Todorov. Last updated 1 years ago.
4.5 match 5 stars 5.37 score 93 scripts
andrisignorell
ModTools:Building Regression and Classification Models
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Maintained by Andri Signorell. Last updated 2 months ago.
5.6 match 2 stars 4.20 score 3 scripts
dtkaplan
LSTbook:Data and Software for "Lessons in Statistical Thinking"
"Lessons in Statistical Thinking" D.T. Kaplan (2014) <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> is a textbook for a first or second course in statistics that embraces data wrangling, causal reasoning, modeling, statistical adjustment, and simulation. 'LSTbook' supports the student-centered, tidy, pipeline-oriented computing style featured in the book.
Maintained by Daniel Kaplan. Last updated 2 days ago.
3.8 match 4 stars 6.29 score 27 scripts
trangdata
treeheatr:Heatmap-Integrated Decision Tree Visualizations
Creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. 'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees.
Maintained by Trang Le. Last updated 2 years ago.
datavizdecision-treesggplotheatmapvisualization
4.0 match 57 stars 5.71 score 18 scripts
jkrijthe
RSSL:Implementations of Semi-Supervised Learning Approaches for Classification
A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.
Maintained by Jesse Krijthe. Last updated 1 years ago.
3.8 match 58 stars 6.05 score 128 scripts 1 dependents
hakeemwahab
cities:Clinical Trials with Intercurrent Events Simulator
Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.
Maintained by Ahmad Hakeem Abdul Wahab. Last updated 2 years ago.
8.7 match 1 stars 2.59 score 39 scriptsfriendly
genridge:Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Maintained by Michael Friendly. Last updated 4 months ago.
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
4.5 match 4 stars 4.84 score 69 scripts
longhaisk
HTLR:Bayesian Logistic Regression with Heavy-Tailed Priors
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.
Maintained by Longhai Li. Last updated 4 months ago.
bayesianclassificationhigh-dimensional-datamachine-learningmcmcopenblascppopenmp
4.0 match 10 stars 5.18 score 7 scripts
bnaras
bcaboot:Bias Corrected Bootstrap Confidence Intervals
Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).
Maintained by Balasubramanian Narasimhan. Last updated 4 years ago.
4.0 match 17 stars 5.15 score 14 scripts 2 dependentslightbluetitan
timeSeriesDataSets:Time Series Data Sets
Provides a diverse collection of time series datasets spanning various fields such as economics, finance, energy, healthcare, and more. Designed to support time series analysis in R by offering datasets from multiple disciplines, making it a valuable resource for researchers and analysts.
Maintained by Renzo Caceres Rossi. Last updated 6 months ago.
3.4 match 10 stars 5.71 score 103 scripts
simonpcouch
readmission:Hospital Readmission Data for Patients with Diabetes
Clinical care data from 130 U.S. hospitals in the years 1999-2008. Each row describes an "encounter" with a patient with diabetes, including variables on demographics, medications, patient history, diagnostics, payment, and readmission.
Maintained by Simon Couch. Last updated 1 years ago.
7.1 match 1 stars 2.70 score 10 scripts
garthtarr
mplot:Graphical Model Stability and Variable Selection Procedures
Model stability and variable inclusion plots [Mueller and Welsh (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008, <doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
Maintained by Garth Tarr. Last updated 4 years ago.
4.0 match 12 stars 4.70 score 42 scripts
alexpate30
rcprd:Extraction and Management of Clinical Practice Research Datalink Data
Simplify the process of extracting and processing Clinical Practice Research Datalink (CPRD) data in order to build datasets ready for statistical analysis. This process is difficult in 'R', as the raw data is very large and cannot be read into the R workspace. 'rcprd' utilises 'RSQLite' to create 'SQLite' databases which are stored on the hard disk. These are then queried to extract the required information for a cohort of interest, and create datasets ready for statistical analysis. The processes follow closely that from the 'rEHR' package, see Springate et al., (2017) <doi:10.1371/journal.pone.0171784>.
Maintained by Alexander Pate. Last updated 20 days ago.
3.1 match 2 stars 5.48 score 5 scriptscran
elasticnet:Elastic-Net for Sparse Estimation and Sparse PCA
Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for doing sparse PCA.
Maintained by Hui Zou. Last updated 5 years ago.
4.0 match 1 stars 4.19 score 21 dependents
ekstroem
MethComp:Analysis of Agreement in Method Comparison Studies
Methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.
Maintained by Claus Thorn Ekstrøm. Last updated 5 months ago.
3.6 match 1 stars 4.63 score 86 scripts
meadhbh-oneill
smoothic:Variable Selection Using a Smooth Information Criterion
Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. <arXiv:2212.07317>. This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". <doi:10.1007/s11222-023-10204-8>.
Maintained by Meadhbh ONeill. Last updated 2 years ago.
4.5 match 1 stars 3.70 score 3 scripts
eddelbuettel
rfoaas:R Interface to 'FOAAS'
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Maintained by Dirk Eddelbuettel. Last updated 5 months ago.
3.0 match 28 stars 5.23 score 6 scripts
profpetrie
regclass:Tools for an Introductory Class in Regression and Modeling
Contains basic tools for visualizing, interpreting, and building regression models. It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9 <https://titles.cognella.com/introduction-to-regression-and-modeling-with-r-9781631892509>.
Maintained by Adam Petrie. Last updated 5 years ago.
4.0 match 3.90 score 301 scripts 1 dependents
kelliejarcher
glmnetcr:Fit a Penalized Constrained Continuation Ratio Model for Predicting an Ordinal Response
Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
Maintained by Kellie J. Archer. Last updated 3 years ago.
3.3 match 4.61 score 27 scripts 1 dependents
vmonaco
frailtySurv:General Semiparametric Shared Frailty Model
Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models.
Maintained by Vinnie Monaco. Last updated 2 years ago.
3.8 match 12 stars 4.01 score 17 scripts
manueleleonelli
bnmonitor:An Implementation of Sensitivity Analysis in Bayesian Networks
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.
Maintained by Manuele Leonelli. Last updated 6 months ago.
3.8 match 3 stars 3.92 score 14 scriptsbioc
biosigner:Signature discovery from omics data
Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.
Maintained by Etienne A. Thevenot. Last updated 5 months ago.
classificationfeatureextractiontranscriptomicsproteomicsmetabolomicslipidomicsmassspectrometry
3.5 match 4.00 score 10 scripts
mcol
hsstan:Hierarchical Shrinkage Stan Models for Biomarker Selection
Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).
Maintained by Marco Colombo. Last updated 1 years ago.
bayesianfeature-selectionmcmccpp
3.8 match 7 stars 3.66 score 13 scripts
tjebo
eyedata:Open Source Ophthalmic Data Sets Curated for R
Open source data allows for reproducible research and helps advance our knowledge. The purpose of this package is to collate open source ophthalmic data sets curated for direct use. This is real life data of people with intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF), due to age-related macular degeneration or diabetic macular edema. Associated publications of the data sets: Fu et al. (2020) <doi:10.1001/jamaophthalmol.2020.5044>, Moraes et al (2020) <doi:10.1016/j.ophtha.2020.09.025>, Fasler et al. (2019) <doi:10.1136/bmjopen-2018-027441>, Arpa et al. (2020) <doi:10.1136/bjophthalmol-2020-317161>, Kern et al. 2020, <doi:10.1038/s41433-020-1048-0>.
Maintained by Tjebo Heeren. Last updated 4 years ago.
3.9 match 4 stars 3.48 score 15 scripts
khliland
ER:Effect + Residual Modelling
Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.
Maintained by Kristian Hovde Liland. Last updated 2 years ago.
4.5 match 3.00 score 1 scripts
bayesstats
jarbes:Just a Rather Bayesian Evidence Synthesis
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Maintained by Pablo Emilio Verde. Last updated 3 months ago.
7.0 match 1 stars 1.91 score 27 scripts
uqwang
chest:Change-in-Estimate Approach to Assess Confounding Effects
Applies the change-in-effect estimate method to assess confounding effects in medical and epidemiological research (Greenland & Pearce (2016) <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' package has functions for linear regression, logistic regression, negative binomial regression, Cox proportional hazards model and conditional logistic regression.
Maintained by Zhiqiang Wang. Last updated 2 years ago.
4.7 match 2.78 score 12 scriptskelliejarcher
glmpathcr:Fit a Penalized Continuation Ratio Model for Predicting an Ordinal Response
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
Maintained by Kellie J. Archer. Last updated 3 years ago.
3.3 match 3.81 score 13 scripts
sestelo
FWDselect:Selecting Variables in Regression Models
A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric).
Maintained by Marta Sestelo. Last updated 9 years ago.
feature-engineeringfeature-selectionmachine-learning-algorithmsnonparametricregresssionvariable-importancevariable-selection
4.5 match 2 stars 2.78 score 30 scripts
great-northern-diver
loon.data:Data Used to Illustrate 'Loon' Functionality
Data used as examples in the 'loon' package.
Maintained by R. Wayne Oldford. Last updated 4 years ago.
3.8 match 1 stars 3.32 score 14 scripts 1 dependents
ehrlinger
l2boost:Exploring Friedman's Boosting Algorithm for Regularized Linear Regression
Efficient implementation of Friedman's boosting algorithm with l2-loss function and coordinate direction (design matrix columns) basis functions.
Maintained by John Ehrlinger. Last updated 3 years ago.
3.5 match 6 stars 3.48 score 10 scripts
lukketotte
MultSurvTests:Permutation Tests for Multivariate Survival Analysis
Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).
Maintained by Lukas Arnroth. Last updated 4 years ago.
4.5 match 2.70 score 2 scripts
yuanyuanli96
maclogp:Measures of Uncertainty for Model Selection
Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.
Maintained by Yuanyuan Li. Last updated 2 years ago.
4.5 match 1 stars 2.70 score 1 scripts
georgheinze
SurvCorr:Correlation of Bivariate Survival Times
Estimates correlation coefficients with associated confidence limits for bivariate, partially censored survival times. Uses the iterative multiple imputation approach proposed by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate distribution, either on the original time scale or as copula.
Maintained by Georg Heinze. Last updated 2 years ago.
4.5 match 2.70 score 2 scriptsmcol
nestfs:Cross-Validated (Nested) Forward Selection
Implementation of forward selection based on cross-validated linear and logistic regression.
Maintained by Marco Colombo. Last updated 2 years ago.
3.8 match 2 stars 3.04 score 11 scriptscran
robmixglm:Robust Generalized Linear Models (GLM) using Mixtures
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
Maintained by Ken Beath. Last updated 6 months ago.
4.5 match 2.49 score 31 scripts
jinghuazhao
gaawr2:Genetic Association Analysis
It gathers information, meta-data and scripts in a two-part Henry-Stewart talk by Zhao (2009, <doi:10.69645/DCRY5578>), which showcases analysis in aspects such as testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with trait using genetic and statistical models as well as Bayesian implementation, power calculation in study design and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications.
Maintained by Jing Hua Zhao. Last updated 2 days ago.
2.3 match 4.90 scorecran
elrm:Exact Logistic Regression via MCMC
Implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.
Maintained by David Zamar. Last updated 3 months ago.
4.0 match 2.56 score 12 scripts 1 dependents
cathblatter
cblttr:cblttr
This package contains several helper functions for personal use. No guarantee in using the package. If you think some content is usable for you, please mention me.
Maintained by Catherine Blatter. Last updated 10 months ago.
6.0 match 1.70 score
homerhanumat
tigerData:GC Statistics Datasets
A small, informal collection of datasets useful in undergraduate statistics courses.
Maintained by Homer White. Last updated 1 months ago.
4.5 match 2.18 score 6 scriptscran
visualpred:Visualization 2D of Binary Classification Models
Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', 'gbm', 'nnet' and 'svm', presented over two dimensions generated by 'famd' and 'mca' methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses under the 'XAI' paradigm.
Maintained by Javier Portela. Last updated 4 months ago.
3.8 match 2.60 scorecran
gamair:Data for 'GAMs: An Introduction with R'
Data sets and scripts used in the book 'Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.
Maintained by Simon Wood. Last updated 6 years ago.
4.0 match 2.43 score 162 scripts
kogalur
spikeslab:Prediction and Variable Selection Using Spike and Slab Regression
Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.
Maintained by Udaya B. Kogalur. Last updated 3 years ago.
4.0 match 1 stars 2.41 score 43 scripts 2 dependents
uqwang
allestimates:Effect Estimates from All Models
Estimates and plots effect estimates from models with all possible combinations of a list of variables. It can be used for assessing treatment effects in clinical trials or risk factors in bio-medical and epidemiological research. Like Stata command 'confall' (Wang Z (2007) <doi:10.1177/1536867X0700700203> ), 'allestimates' calculates and stores all effect estimates, and plots them against p values or Akaike information criterion (AIC) values. It currently has functions for linear regression: all_lm(), logistic and Poisson regression: all_glm(), and Cox proportional hazards regression: all_cox().
Maintained by Zhiqiang Wang. Last updated 2 years ago.
3.4 match 2.70 score 5 scripts
linlf
pcnetmeta:Patient-Centered Network Meta-Analysis
Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 <doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).
Maintained by Lifeng Lin. Last updated 3 years ago.
3.8 match 1 stars 2.08 score 12 scripts
anotherruisun
wtest:The W-Test for Genetic Interactions Testing
Perform the calculation of W-test, diagnostic checking, calculate minor allele frequency (MAF) and odds ratio.
Maintained by Rui Sun. Last updated 6 years ago.
7.3 match 1.04 score 11 scripts
zhanyq
nlrr:Non-Linear Relative Risk Estimation and Plotting
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Maintained by Yiqiang Zhan. Last updated 9 years ago.
4.5 match 1.70 score 2 scriptscran
Libra:Linearized Bregman Algorithms for Generalized Linear Models
Efficient procedures for fitting the regularization path for linear, binomial, multinomial, Ising and Potts models with lasso, group lasso or column lasso(only for multinomial) penalty. The package uses Linearized Bregman Algorithm to solve the regularization path through iterations. Bregman Inverse Scale Space Differential Inclusion solver is also provided for linear model with lasso penalty.
Maintained by Jiechao Xiong. Last updated 3 years ago.
4.0 match 1.85 score 71 scriptsbayesstats
bamdit:Bayesian Meta-Analysis of Diagnostic Test Data
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Maintained by Pablo Emilio Verde. Last updated 1 months ago.
3.4 match 2.05 score 14 scripts
lionelvoirol
idarps:Datasets and Functions for the Class "Modelling and Data Analysis for Pharmaceutical Sciences"
Provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". The datasets can be used to present various methods of data analysis and statistical modeling. Functions for data visualization are also implemented.
Maintained by Lionel Voirol. Last updated 11 months ago.
4.0 match 1.70 score 6 scripts
nseg4
durhamSLR:The durhamSLR package
Data for Statistical Learning modules at Durham University.
Maintained by Sarah.Heaps. Last updated 2 years ago.
4.0 match 1.70 score
mjderooij
lmap:Logistic Mapping
Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).
Maintained by Mark de Rooij. Last updated 2 months ago.
4.5 match 1.48 score 3 scriptscran
Markovchart:Markov Chain-Based Cost-Optimal Control Charts
Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.
Maintained by Balazs Dobi. Last updated 3 years ago.
3.3 match 2.00 scorecran
care:High-Dimensional Regression and CAR Score Variable Selection
Implements the regression approach of Zuber and Strimmer (2011) "High-dimensional regression and variable selection using CAR scores" SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>. CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available.
Maintained by Korbinian Strimmer. Last updated 3 years ago.
3.5 match 1 stars 1.87 score 19 scripts
ald0405
SangerTools:Tools for Population Health Management Analytics
Created for population health analytics and monitoring. The functions in this package work best when working with patient level Master Patient Index-like datasets . Built to be used by NHS bodies and other health service providers.
Maintained by Asif Laldin. Last updated 1 years ago.
nhsnhs-r-communitypopulation-healthprevalence
1.2 match 5 stars 5.05 score 45 scripts
reealpeppe
glmxdiag:A Collection of Graphic Tools for GLM Diagnostics and some Extensions
Provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by 'VGAM' package), beta regression model (estimated by 'betareg' package) and negative binomial regression model (estimated by 'MASS' package). Since most of functions implemented in 'glmxdiag' already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.
Maintained by Giuseppe Reale. Last updated 3 years ago.
6.0 match 1.00 score
martaaaa
flexOR:Flexible Odds Ratio Curves
Provides flexible odds ratio curves that enable modeling non-linear relationships between continuous predictors and binary outcomes. This package facilitates a deeper understanding of the impact of each continuous predictor on the outcome by presenting results in terms of odds ratio (OR) curves based on splines. These curves allow for comparison against a specified reference value, aiding in the interpretation of the predictor's effect.
Maintained by Marta Azevedo. Last updated 6 months ago.
1.3 match 1 stars 4.60 score 1 scripts
kjhealy
gssrdoc:Document General Social Survey Variable
The General Social Survey (GSS) is a long-running, mostly annual survey of US households. It is administered by the National Opinion Research Center (NORC). This package contains the a tibble with information on the survey variables, together with every variable documented as an R help page. For more information on the GSS see \url{http://gss.norc.org}.
Maintained by Kieran Healy. Last updated 11 months ago.
2.3 match 2.28 score 38 scripts
kwstat
mountainplot:Mountain Plots, Folded Empirical Cumulative Distribution Plots
Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.
Maintained by Kevin Wright. Last updated 8 months ago.
1.3 match 1 stars 3.70 score 5 scriptscran
sMSROC:Assessment of Diagnostic and Prognostic Markers
Provides estimations of the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).
Maintained by Susana Diaz-Coto. Last updated 1 years ago.
4.5 match 1.00 scorecran
datarobot:'DataRobot' Predictive Modeling API
For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.
Maintained by AJ Alon. Last updated 1 years ago.
1.2 match 2 stars 3.48 score
extremestats
DATAstudio:The Research Data Warehouse of Miguel de Carvalho
Pulls together a collection of datasets from Miguel de Carvalho research articles. Including, for example: - de Carvalho (2012) <doi:10.1016/j.jspi.2011.08.016>; - de Carvalho et al (2012) <doi:10.1080/03610926.2012.709905>; - de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>); - de Carvalho and Davison (2014) <doi:10.1080/01621459.2013.872651>; - de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>.
Maintained by Miguel de Carvalho. Last updated 3 years ago.
4.0 match 1.00 score 2 scripts
gianluca-sottile
islasso:The Induced Smoothed Lasso
An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.
Maintained by Gianluca Sottile. Last updated 1 years ago.
4.0 match 1.00 score 2 scriptscran
assist:A Suite of R Functions Implementing Spline Smoothing Techniques
Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.
Maintained by Yuedong Wang. Last updated 2 years ago.
3.6 match 1.00 score
rainers48
tsapp:Time Series, Analysis and Application
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Maintained by Rainer Schlittgen. Last updated 3 years ago.
3.6 match 1.00 score 1 scriptscran
rGV:Analysis of Continuous Glucose Monitor Data
Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time.
Maintained by Evan Olawsky. Last updated 3 months ago.
1.7 match 2 stars 2.00 score 2 scripts
sufyansuleman
InsuSensCalc:Insulin Sensitivity Indices Calculator
It facilitates the calculation of 40 different insulin sensitivity indices based on fasting, oral glucose tolerance test (OGTT), lipid (adipose), and tracer (palmitate and glycerol rate) and dxa (fat mass) measurement values. It enables easy and accurate assessment of insulin sensitivity, critical for understanding and managing metabolic disorders like diabetes and obesity. Indices calculated are described in Gastaldelli (2022). <doi:10.1002/oby.23503> and Lorenzo (2010). <doi:10.1210/jc.2010-1144>.
Maintained by Sufyan Suleman. Last updated 12 months ago.
0.5 match 1 stars 4.00 score 3 scripts
imranshakoor
DataSetsUni:A Collection of Univariate Data Sets
A collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.
Maintained by Muhammad Imran. Last updated 2 years ago.
1.9 match 1.00 score 1 scripts
supercheng971264
T2DFitTailor:Tailor the Exercise Plans and Visualize the Outcome for T2D Patients
A system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and 'T2DFitTailor' details the exercise plan and predicts the intervention's effectiveness.
Maintained by Cheng Liu. Last updated 11 months ago.
0.5 match 1.00 score