R-universe search: robustness

msalibian

RobStatTM:Robust Statistics: Theory and Methods

Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

Maintained by Matias Salibian-Barrera. Last updated 2 days ago.

robust robust-estimation robust-regression robust-statistics robustness statistics fortran openblas

77.5 match 17 stars 10.23 score 84 scripts 8 dependents

valentint

robust:Port of the S+ "Robust Library"

Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.

Maintained by Valentin Todorov. Last updated 7 months ago.

fortran openblas

104.2 match 7.52 score 572 scripts 8 dependents

r-forge

robustbase:Basic Robust Statistics

"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.

Maintained by Martin Maechler. Last updated 4 months ago.

fortran openblas

57.7 match 13.33 score 1.7k scripts 480 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.

fortran openblas

57.1 match 2 stars 10.57 score 484 scripts 96 dependents

jepusto

clubSandwich:Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections

Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), geeglm() (from package 'geepack'), ivreg() (from package 'AER'), ivreg() (from package 'ivreg' when estimated by ordinary least squares), plm() (from package 'plm'), gls() and lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from 'metafor').

Maintained by James Pustejovsky. Last updated 15 days ago.

32.7 match 48 stars 11.25 score 656 scripts 4 dependents

tobiasschoch

robsurvey:Robust Survey Statistics Estimation

Robust (outlier-resistant) estimators of finite population characteristics like of means, totals, ratios, regression, etc. Available methods are M- and GM-estimators of regression, weight reduction, trimming, and winsorization. The package extends the 'survey' <https://CRAN.R-project.org/package=survey> package.

Maintained by Tobias Schoch. Last updated 3 months ago.

openblas

42.3 match 9 stars 6.16 score 5 scripts

indrajeetpatil

statsExpressions:Tidy Dataframes and Expressions with Statistical Details

Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 20 days ago.

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

19.0 match 312 stars 10.97 score 146 scripts 2 dependents

r-forge

RobLox:Optimally Robust Influence Curves and Estimators for Location and Scale

Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).

Maintained by Matthias Kohl. Last updated 2 months ago.

43.7 match 4.40 score 70 scripts 1 dependents

aalfons

robmed:(Robust) Mediation Analysis

Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2024) <doi:10.31234/osf.io/2hqdy>.

Maintained by Andreas Alfons. Last updated 15 days ago.

29.1 match 6 stars 6.35 score 31 scripts 1 dependents

ycroissant

plm:Linear Models for Panel Data

A set of estimators for models and (robust) covariance matrices, and tests for panel data econometrics, including within/fixed effects, random effects, between, first-difference, nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators with common factors, variable coefficients and limited dependent variables models. Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>), Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), Panel Data Econometrics with R (<doi:10.1002/9781119504641>).

Maintained by Kevin Tappe. Last updated 7 hours ago.

15.0 match 59 stars 12.06 score 39 dependents

easystats

correlation:Methods for Correlation Analysis

Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.

Maintained by Brenton M. Wiernik. Last updated 12 days ago.

bayesian bayesian-correlations biserial cor correlation correlation-analysis correlations easystats gamma gaussian-graphical-models hacktoberfest matrix multilevel-correlations outliers partial partial-correlations regression robust spearman

11.0 match 439 stars 14.23 score 672 scripts 10 dependents

r-forge

WRS2:A Collection of Robust Statistical Methods

A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.

Maintained by Patrick Mair. Last updated 3 months ago.

17.4 match 8.96 score 402 scripts 7 dependents

fbartos

RoBMA:Robust Bayesian Meta-Analyses

A framework for estimating ensembles of meta-analytic and meta-regression models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, <doi:10.1037/met0000405>). Users can define a wide range of prior distributions for + the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.

Maintained by František Bartoš. Last updated 1 months ago.

meta-analysis model-averaging publication-bias jags openblas cpp

22.1 match 9 stars 6.97 score 53 scripts

zhuwang46

mpath:Regularized Linear Models

Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2020) <arXiv:2010.02848>.

Maintained by Zhu Wang. Last updated 3 years ago.

fortran openblas

22.8 match 1 stars 6.67 score 131 scripts 4 dependents

wviechtb

metafor:Meta-Analysis Package for R

A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.

Maintained by Wolfgang Viechtbauer. Last updated 20 hours ago.

meta-analysis mixed-effects multilevel-models multivariate

9.0 match 246 stars 16.30 score 4.9k scripts 92 dependents

easystats

parameters:Processing of Model Parameters

Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).

Maintained by Daniel Lüdecke. Last updated 2 days ago.

beta bootstrap ci confidence-intervals data-reduction easystats fa feature-extraction feature-reduction hacktoberfest parameters pca pvalues regression-models robust-statistics standardize standardized-estimates statistical-models

9.3 match 453 stars 15.65 score 1.8k scripts 56 dependents

braverock

PerformanceAnalytics:Econometric Tools for Performance and Risk Analysis

Collection of econometric functions for performance and risk analysis. In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.

Maintained by Brian G. Peterson. Last updated 3 months ago.

8.5 match 222 stars 15.93 score 4.8k scripts 20 dependents

pedrohcgs

DRDID:Doubly Robust Difference-in-Differences Estimators

Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

Maintained by Pedro H. C. SantAnna. Last updated 5 months ago.

cpp

15.3 match 92 stars 8.88 score 133 scripts 5 dependents

valentint

pcaPP:Robust PCA by Projection Pursuit

Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.

Maintained by Valentin Todorov. Last updated 7 months ago.

openblas cpp

12.4 match 1 stars 10.56 score 186 scripts 351 dependents

andrisignorell

DescTools:Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Maintained by Andri Signorell. Last updated 9 days ago.

fortran cpp

7.8 match 87 stars 16.68 score 7.7k scripts 99 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 2 days ago.

multivariate-time-to-event survival-analysis time-to-event fortran openblas cpp

9.6 match 14 stars 13.47 score 236 scripts 42 dependents

bsaul

geex:An API for M-Estimation

Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.

Maintained by Bradley Saul. Last updated 10 months ago.

asymptotics covariance-estimates covariance-estimation estimate-parameters estimating-equations estimation inference m-estimation robust sandwich

16.5 match 8 stars 7.70 score 131 scripts 2 dependents

aalfons

robustHD:Robust Methods for High-Dimensional Data

Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).

Maintained by Andreas Alfons. Last updated 9 months ago.

openblas cpp openmp

16.4 match 10 stars 7.06 score 174 scripts 8 dependents

chabert-liddell

robber:Using Block Model to Estimate the Robustness of Ecological Network

Implementation of a variety of methods to compute the robustness of ecological interaction networks with binary interactions as described in <doi:10.1002/env.2709>. In particular, using the Stochastic Block Model and its bipartite counterpart, the Latent Block Model to put a parametric model on the network, allows the comparison of the robustness of networks differing in species richness and number of interactions. It also deals with networks that are partially sampled and/or with missing values.

Maintained by Saint-Clair Chabert-Liddell. Last updated 1 years ago.

ecological-network robber robustness

30.9 match 1 stars 3.70 score 4 scripts

matthias-da

robCompositions:Compositional Data Analysis

Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Maintained by Matthias Templ. Last updated 26 days ago.

cpp

11.6 match 11 stars 9.19 score 226 scripts 2 dependents

r-forge

RobAStBase:Robust Asymptotic Statistics

Base S4-classes and functions for robust asymptotic statistics.

Maintained by Matthias Kohl. Last updated 2 months ago.

20.8 match 4.96 score 64 scripts 4 dependents

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.

openblas cpp

18.5 match 2 stars 5.58 score 27 scripts 3 dependents

asheshrambachan

HonestDiD:Robust Inference in Difference-in-Differences and Event Study Designs

Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) <doi:10.1093/restud/rdad018>, "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets.

Maintained by Ashesh Rambachan. Last updated 17 days ago.

difference-in-differences event-studies robust-inference

14.0 match 195 stars 7.11 score 63 scripts

cran

fBasics:Rmetrics - Markets and Basic Statistics

Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.

Maintained by Georgi N. Boshnakov. Last updated 7 months ago.

13.9 match 2 stars 7.11 score 129 dependents

smartdata-analysis-and-statistics

precmed:Precision Medicine

A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.

Maintained by Thomas Debray. Last updated 5 months ago.

precision-medicine

22.6 match 4 stars 4.20 score 4 scripts

dakep

pense:Penalized Elastic Net S/MM-Estimator of Regression

Robust penalized (adaptive) elastic net S and M estimators for linear regression. The methods are proposed in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <https://projecteuclid.org/euclid.aoas/1574910036>. The package implements the extensions and algorithms described in Kepplinger, D. (2020) <doi:10.14288/1.0392915>.

Maintained by David Kepplinger. Last updated 8 months ago.

linear-regression pense regression robust-regresssion robust-statistics openblas cpp openmp

15.5 match 4 stars 6.06 score 48 scripts

kollerma

robustlmm:Robust Linear Mixed Effects Models

Implements the Robust Scoring Equations estimator to fit linear mixed effects models robustly. Robustness is achieved by modification of the scoring equations combined with the Design Adaptive Scale approach.

Maintained by Manuel Koller. Last updated 1 years ago.

openblas cpp

10.3 match 28 stars 8.79 score 138 scripts

bioc

preprocessCore:A collection of pre-processing functions

A library of core preprocessing routines.

Maintained by Ben Bolstad. Last updated 5 months ago.

infrastructure openblas

7.5 match 19 stars 12.03 score 1.8k scripts 204 dependents

coffeemuggler

EMMAgeo:End-Member Modelling of Grain-Size Data

End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.

Maintained by Michael Dietze. Last updated 5 years ago.

20.3 match 10 stars 4.13 score 27 scripts

benkeser

drtmle:Doubly-Robust Nonparametric Estimation and Inference

Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

Maintained by David Benkeser. Last updated 2 years ago.

causal-inference ensemble-learning iptw statistical-inference tmle

11.9 match 19 stars 6.89 score 90 scripts 1 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 8 days ago.

linear-hypotheses matrices multivariate-linear-models plot repeated-measure-designs visualizing-hypothesis-tests

7.1 match 9 stars 11.49 score 1.1k scripts 7 dependents

stscl

gdverse:Analysis of Spatial Stratified Heterogeneity

Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.

Maintained by Wenbo Lv. Last updated 1 days ago.

geographical-detector geoinformatics geospatial-analysis spatial-statistics spatial-stratified-heterogeneity cpp

8.8 match 32 stars 9.07 score 41 scripts 2 dependents

r-forge

ROptEst:Optimally Robust Estimation

R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.

Maintained by Matthias Kohl. Last updated 2 months ago.

18.5 match 4.26 score 50 scripts 1 dependents

valentint

rrcovHD:Robust Multivariate Methods for High Dimensional Data

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

Maintained by Valentin Todorov. Last updated 7 months ago.

cpp

23.2 match 3.39 score 49 scripts

valentint

tclust:Robust Trimmed Clustering

Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011) <doi:10.1007/s11222-010-9194-z> and others.

Maintained by Valentin Todorov. Last updated 25 days ago.

openblas cpp openmp

9.5 match 3 stars 8.02 score 72 scripts 3 dependents

vlyubchich

lawstat:Tools for Biostatistics, Public Policy, and Law

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

Maintained by Yulia R. Gel. Last updated 2 years ago.

9.9 match 7.17 score 484 scripts 6 dependents

jlaake

RMark:R Code for Mark Analysis

An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at <http://www.phidot.org/software/mark/downloads/> but is not open source.

Maintained by Jeff Laake. Last updated 3 years ago.

14.4 match 4.90 score 366 scripts 4 dependents

treynkens

rospca:Robust Sparse PCA using the ROSPCA Algorithm

Implementation of robust sparse PCA using the ROSPCA algorithm of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.

Maintained by Tom Reynkens. Last updated 4 months ago.

robust-pca sparse-data

14.8 match 13 stars 4.77 score 45 scripts

paulnorthrop

chandwich:Chandler-Bate Sandwich Loglikelihood Adjustment

Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test.

Maintained by Paul J. Northrop. Last updated 2 years ago.

clustered-data clusters composite-likelihood independence-loglikelihood mle robust sandwich statistical-inference

11.8 match 4 stars 5.88 score 18 scripts 7 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

5.3 match 15 stars 12.60 score 7.7k scripts 295 dependents

kkholst

targeted:Targeted Inference

Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Maintained by Klaus K. Holst. Last updated 1 months ago.

causal-inference double-robust estimation semiparametric-estimation statistics openblas cpp openmp

9.2 match 11 stars 7.20 score 30 scripts 1 dependents

benkeser

drord:Doubly-Robust Estimators for Ordinal Outcomes

Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes (Benkeser, Diaz, Luedtke 2020 <doi:10.1111/biom.13377>)

Maintained by David Benkeser. Last updated 4 years ago.

causal-inference covid-19 double-robust mann-whitney ordinal-regression

15.0 match 4 stars 4.38 score 12 scripts

filzmoserp

chemometrics:Multivariate Statistical Analysis in Chemometrics

R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

Maintained by Peter Filzmoser. Last updated 2 years ago.

9.6 match 4 stars 6.72 score 213 scripts 4 dependents

easystats

insight:Easy Access to Model Information for Various Model Objects

A tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. 'insight' mainly revolves around two types of functions: Functions that find (the names of) information, starting with 'find_', and functions that get the underlying data, starting with 'get_'. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.

Maintained by Daniel Lüdecke. Last updated 4 days ago.

easystats hacktoberfest insight models names predictors random

3.7 match 412 stars 17.24 score 568 scripts 210 dependents

japal

MALDIrppa:MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis

Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) <doi:10.1093/bioinformatics/btx628>).

Maintained by Javier Palarea-Albaladejo. Last updated 1 years ago.

mass-spectrometry pre-processing

10.5 match 2 stars 6.06 score 32 scripts 1 dependents

bioc

affy:Methods for Affymetrix Oligonucleotide Arrays

The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.

Maintained by Robert D. Shear. Last updated 2 months ago.

microarray onechannel preprocessing

5.6 match 11.12 score 2.5k scripts 98 dependents

asa12138

MetaNet:Network Analysis for Omics Data

Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.

Maintained by Chen Peng. Last updated 11 days ago.

dataimport network analysis omics software visualization

11.3 match 13 stars 5.51 score 9 scripts

jeroen

jsonlite:A Simple and Robust JSON Parser and Generator for R

A reasonably fast JSON parser and generator, optimized for statistical data and the web. Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.

Maintained by Jeroen Ooms. Last updated 22 days ago.

json parser

2.9 match 384 stars 21.15 score 27k scripts 8.6k dependents

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

7.9 match 15 stars 7.68 score 59 scripts 4 dependents

jorischau

gslnls:GSL Multi-Start Nonlinear Least-Squares Fitting

An R interface to weighted nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust nonlinear regression can be performed using various robust loss functions, in which case the optimization problem is solved by iterative reweighted least squares (IRLS). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.

Maintained by Joris Chau. Last updated 2 months ago.

gnu-scientific-library gsl levenberg-marquardt multi-start nonlinear-least-squares nonlinear-regression robust-regresssion fortran glibc

10.1 match 15 stars 6.03 score 35 scripts 1 dependents

core-bioinformatics

ClustAssess:Tools for Assessing Clustering

A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.

Maintained by Andi Munteanu. Last updated 1 months ago.

software singlecell rnaseq atacseq normalization preprocessing dimensionreduction visualization qualitycontrol clustering classification annotation geneexpression differentialexpression bioinformatics genomics machine-learning parameter-optimization robustness single-cell unsupervised-learning cpp

10.5 match 23 stars 5.70 score 18 scripts

kosukeimai

MatchIt:Nonparametric Preprocessing for Parametric Causal Inference

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)

Maintained by Noah Greifer. Last updated 1 days ago.

cpp openmp

4.0 match 220 stars 15.03 score 2.4k scripts 21 dependents

cran

grf:Generalized Random Forests

Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.

Maintained by Erik Sverdrup. Last updated 4 months ago.

cpp

10.2 match 5.83 score 1.2k scripts 14 dependents

nt-williams

lmtp:Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Maintained by Nicholas Williams. Last updated 8 days ago.

causal-inference censored-data longitudinal-data machine-learning modified-treatment-policy nonparametric-statistics precision-medicine robust-statistics statistics stochastic-interventions survival-analysis targeted-learning

9.3 match 64 stars 6.37 score 91 scripts

yqzhong7

AIPW:Augmented Inverse Probability Weighting

The 'AIPW' package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. doi: 10.1093/aje/kwab207". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Maintained by Yongqi Zhong. Last updated 6 months ago.

causal-inference machine-learning robust-estimators

8.0 match 24 stars 7.35 score 31 scripts 1 dependents

statimagcoll

RESI:Robust Effect Size Index (RESI) Estimation

Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020) <doi:10.1007/s11336-020-09698-2>.

Maintained by Megan Jones. Last updated 13 days ago.

13.5 match 4.30 score 20 scripts

r-forge

sandwich:Robust Covariance Matrix Estimators

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.

Maintained by Achim Zeileis. Last updated 2 months ago.

3.9 match 14.92 score 11k scripts 887 dependents

cran

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 16 days ago.

5.4 match 19 stars 10.53 score 11k dependents

appliedstat

rQCC:Robust Quality Control Chart

Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g, h, and t charts are also easily constructed. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1091319).

Maintained by Chanseok Park. Last updated 1 years ago.

control-chart goodness-of-fit r-language weibull

12.0 match 2 stars 4.70 score 3 scripts

zackfisher

robumeta:Robust Variance Meta-Regression

Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.

Maintained by Zachary Fisher. Last updated 4 years ago.

7.1 match 8 stars 7.75 score 178 scripts 4 dependents

biometry

bipartite:Visualising Bipartite Networks and Calculating Some (Ecological) Indices

Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.

Maintained by Carsten F. Dormann. Last updated 6 days ago.

cpp

5.0 match 37 stars 10.93 score 592 scripts 15 dependents

merliseclyde

BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Maintained by Merlise Clyde. Last updated 4 months ago.

bayesian bayesian-inference generalized-linear-models linear-regression logistic-regression mcmc model-selection poisson-regression predictive-modeling regression variable-selection fortran openblas

5.1 match 44 stars 10.81 score 420 scripts 3 dependents

thie1e

cutpointr:Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included. For an overview of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.

Maintained by Christian Thiele. Last updated 3 months ago.

bootstrapping cutpoint-optimization roc-curve cpp

5.1 match 88 stars 10.44 score 322 scripts 1 dependents

finyang

RRRR:Online Robust Reduced-Rank Regression Estimation

Methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>. The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>. The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.

Maintained by Yangzhuoran Fin Yang. Last updated 2 years ago.

12.7 match 3 stars 4.18 score 10 scripts

bioc

RolDE:RolDE: Robust longitudinal Differential Expression

RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings.

Maintained by Medical Bioinformatics Centre. Last updated 5 months ago.

statisticalmethod software timecourse regression proteomics differentialexpression

10.1 match 5 stars 5.18 score 1 scripts

valentint

rrcovNA:Scalable Robust Estimators with High Breakdown Point for Incomplete Data

Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point for Incomplete Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).

Maintained by Valentin Todorov. Last updated 3 months ago.

fortran

13.9 match 1 stars 3.77 score 59 scripts

thinkr-open

golem:A Framework for Robust Shiny Applications

An opinionated framework for building a production-ready 'Shiny' application. This package contains a series of tools for building a robust 'Shiny' application from start to finish.

Maintained by Colin Fay. Last updated 7 months ago.

golemverse hacktoberfest shiny shiny-apps shiny-r shinyapps

3.7 match 921 stars 14.23 score 167 scripts 62 dependents

ctruciosm

RobGARCHBoot:Robust Bootstrap Forecast Densities for GARCH Models

Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.

Maintained by Carlos Trucios. Last updated 4 years ago.

cpp

16.3 match 3 stars 3.18 score 1 scripts

changwn

RobMixReg:Robust Mixture Regression

Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>. NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>. Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>. Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.

Maintained by Wennan Chang. Last updated 7 days ago.

19.0 match 2.70 score 7 scripts

tobiasschoch

wbacon:Weighted BACON Algorithms

The BACON algorithms are methods for multivariate outlier nomination (detection) and robust linear regression by Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension to weighted problems is due to Beguin and Hulliger (2008) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see also <doi:10.21105/joss.03238>.

Maintained by Tobias Schoch. Last updated 6 months ago.

outlier outlier-detection robust-regression statistics openblas openmp

12.6 match 2 stars 4.00 score 8 scripts

paulnorthrop

lax:Loglikelihood Adjustment for Extreme Value Models

Performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages 'eva' <https://cran.r-project.org/package=eva>, 'evd' <https://cran.r-project.org/package=evd>, 'evir' <https://cran.r-project.org/package=evir>, 'extRemes' <https://cran.r-project.org/package=extRemes>, 'fExtremes' <https://cran.r-project.org/package=fExtremes>, 'ismev' <https://cran.r-project.org/package=ismev>, 'mev' <https://cran.r-project.org/package=mev>, 'POT' <https://cran.r-project.org/package=POT> and 'texmex' <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the 'chandwich' package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the 'sandwich' package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.

Maintained by Paul J. Northrop. Last updated 1 years ago.

clustered-data clusters composite-likelihood evd extreme-value-analysis extreme-value-statistics extremes independence-loglikelihood loglikelihood-adjustment mle pot regression regression-modelling robust sandwich sandwich-estimator

11.8 match 3 stars 4.29 score 13 scripts

convexfi

fitHeavyTail:Mean and Covariance Matrix Estimation under Heavy Tails

Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).

Maintained by Daniel P. Palomar. Last updated 2 years ago.

cauchy covariance-estimation covariance-matrix heavy-tailed-distributions outliers robust-estimation student-t tyler

8.0 match 22 stars 6.27 score 28 scripts 1 dependents

bioc

msqrob2:Robust statistical inference for quantitative LC-MS proteomics

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Maintained by Lieven Clement. Last updated 18 days ago.

proteomics massspectrometry differentialexpression multiplecomparison regression experimentaldesign software immunooncology normalization timecourse preprocessing

7.2 match 10 stars 6.94 score 83 scripts

statdivlab

corncob:Count Regression for Correlated Observations with the Beta-Binomial

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.

Maintained by Amy D Willis. Last updated 6 months ago.

5.1 match 105 stars 9.64 score 248 scripts 1 dependents

uncertaintyquantification

RobustGaSP:Robust Gaussian Stochastic Process Emulation

Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Maintained by Mengyang Gu. Last updated 1 years ago.

cpp

20.5 match 2.35 score 75 scripts 1 dependents

mmaechler

sfsmisc:Utilities from 'Seminar fuer Statistik' ETH Zurich

Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().

Maintained by Martin Maechler. Last updated 5 months ago.

4.4 match 11 stars 10.87 score 566 scripts 119 dependents

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 5 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

3.4 match 13.81 score 16k scripts 585 dependents

philchalmers

SimDesign:Structure for Organizing Monte Carlo Simulation Designs

Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.

Maintained by Phil Chalmers. Last updated 4 hours ago.

monte-carlo-simulation simulation simulation-framework

3.5 match 62 stars 13.36 score 253 scripts 46 dependents

zdebruine

RcppML:Rcpp Machine Learning Library

Fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

Maintained by Zach DeBruine. Last updated 2 years ago.

clustering matrix-factorization nmf rcpp rcppeigen sparse-matrix cpp openmp

4.5 match 104 stars 10.53 score 125 scripts 46 dependents

loukiaspin

rnmamod:Bayesian Network Meta-Analysis with Missing Participants

A comprehensive suite of functions to perform and visualise pairwise and network meta-analysis with aggregate binary or continuous missing participant outcome data. The package covers core Bayesian one-stage models implemented in a systematic review with multiple interventions, including fixed-effect and random-effects network meta-analysis, meta-regression, evaluation of the consistency assumption via the node-splitting approach and the unrelated mean effects model (original and revised model proposed by Spineli, (2022) <doi:10.1177/0272989X211068005>), and sensitivity analysis (see Spineli et al., (2021) <doi:10.1186/s12916-021-02195-y>). Missing participant outcome data are addressed in all models of the package (see Spineli, (2019) <doi:10.1186/s12874-019-0731-y>, Spineli et al., (2019) <doi:10.1002/sim.8207>, Spineli, (2019) <doi:10.1016/j.jclinepi.2018.09.002>, and Spineli et al., (2021) <doi:10.1002/jrsm.1478>). The robustness to primary analysis results can also be investigated using a novel intuitive index (see Spineli et al., (2021) <doi:10.1177/0962280220983544>). Methods to evaluate the transitivity assumption quantitatively are provided (see Spineli, (2024) <doi:10.1186/s12874-024-02436-7>). A novel index to facilitate interpretation of local inconsistency is also available (see Spineli, (2024) <doi:0.1186/s13643-024-02680-4>) The package also offers a rich, user-friendly visualisation toolkit that aids in appraising and interpreting the results thoroughly and preparing the manuscript for journal submission. The visualisation tools comprise the network plot, forest plots, panel of diagnostic plots, heatmaps on the extent of missing participant outcome data in the network, league heatmaps on estimation and prediction, rankograms, Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of robustness, barplot of Kullback-Leibler divergence, heatmap of comparison dissimilarities and dendrogram of comparison clustering. The package also allows the user to export the results to an Excel file at the working directory.

Maintained by Loukia Spineli. Last updated 9 days ago.

jags cpp

7.1 match 5 stars 6.64 score 12 scripts

kisungyou

Rdimtools:Dimension Reduction and Estimation Methods

We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

Maintained by Kisung You. Last updated 2 years ago.

dimension-estimation dimension-reduction manifold-learning subspace-learning openblas cpp openmp

5.6 match 52 stars 8.37 score 186 scripts 8 dependents

ucl

rmcmc:Robust Markov Chain Monte Carlo Methods

Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface <https://roualdes.github.io/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then 'BridgeStan' can be used to specify the target distribution to sample from.

Maintained by Matthew M. Graham. Last updated 13 days ago.

approximate-inference mcmc

8.0 match 5 stars 5.85 score 8 scripts

unina-sfere

funcharts:Functional Control Charts

Provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>, Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>, Capezza et al. (2024) <doi:10.1080/00224065.2024.2383674>, Centofanti et al. (2022) <doi:10.48550/arXiv.2205.06256>. The package is thoroughly illustrated in the paper of Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.

Maintained by Christian Capezza. Last updated 2 days ago.

openblas cpp

7.0 match 2 stars 6.67 score 168 scripts

spkaluzny

robustarima:Robust ARIMA Modeling

Functions for fitting a linear regression model with ARIMA errors using a filtered tau-estimate. The methodology is described in Maronna et al (2017, ISBN:9781119214687).

Maintained by Stephen Kaluzny. Last updated 6 months ago.

arima robust-statistics time-series-analysis fortran openblas

14.3 match 3.23 score 17 scripts

s3alfisc

summclust:Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors

Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <arXiv:2205.03288v1>).

Maintained by Alexander Fischer. Last updated 2 years ago.

clustered-standard-errors fixest linear-regression robust-inference

7.5 match 6 stars 6.16 score 53 scripts 3 dependents

jkurle

robust2sls:Outlier Robust Two-Stage Least Squares Inference and Testing

An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Maintained by Jonas Kurle. Last updated 2 years ago.

10.3 match 1 stars 4.43 score 18 scripts

declaredesign

estimatr:Fast Estimators for Design-Based Inference

Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.

Maintained by Graeme Blair. Last updated 1 months ago.

cpp

3.9 match 133 stars 11.58 score 1.7k scripts 11 dependents

johnnyzhz

rsem:Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.

Maintained by Zhiyong Zhang. Last updated 2 years ago.

15.7 match 2.89 score 13 scripts 2 dependents

kbose28

FarmSelect:Factor Adjusted Robust Model Selection

Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.

Maintained by Kaizheng Wang. Last updated 6 years ago.

openblas cpp

10.0 match 7 stars 4.54 score 8 scripts

rstudio

shinytest2:Testing for Shiny Applications

Automated unit testing of Shiny applications through a headless 'Chromium' browser.

Maintained by Barret Schloerke. Last updated 1 months ago.

cpp

3.8 match 108 stars 12.08 score 704 scripts 1 dependents

bflammers

ANN2:Artificial Neural Networks for Anomaly Detection

Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning.

Maintained by Bart Lammers. Last updated 4 years ago.

anomaly-detection artificial-neural-networks autoencoders neural-networks robust-statistics openblas cpp openmp

8.0 match 13 stars 5.59 score 60 scripts

rogih

tsqn:Applications of the Qn Estimator to Time Series (Univariate and Multivariate)

Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.

Maintained by Higor Cotta. Last updated 6 years ago.

19.9 match 1 stars 2.23 score 17 scripts

mthrun

DataVisualizations:Visualizations of High-Dimensional Data

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Maintained by Michael Thrun. Last updated 2 months ago.

cpp

5.8 match 7 stars 7.72 score 118 scripts 7 dependents

msalibian

RBF:Robust Backfitting

A robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.

Maintained by Matias Salibian-Barrera. Last updated 2 years ago.

8.6 match 2 stars 5.11 score 13 scripts

jacob-long

jtools:Analysis and Presentation of Social Scientific Data

This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Support for models produced by the survey and lme4 packages are points of emphasis.

Maintained by Jacob A. Long. Last updated 6 months ago.

social-sciences

3.0 match 167 stars 14.48 score 4.0k scripts 14 dependents

bayesplay

bayesplay:The Bayes Factor Playground

A lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors.

Maintained by Lincoln John Colling. Last updated 1 years ago.

bayes bayesian bayesian-statistics

7.8 match 6 stars 5.54 score 23 scripts

ubod

rococo:Robust Rank Correlation Coefficient and Test

Provides the robust gamma rank correlation coefficient as introduced by Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026> along with a permutation-based rank correlation test. The rank correlation coefficient and the test are explicitly designed for dealing with noisy numerical data.

Maintained by Ulrich Bodenhofer. Last updated 11 months ago.

cpp

9.9 match 4.32 score 21 scripts

tobiasschoch

rsae:Robust Small Area Estimation

Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.

Maintained by Tobias Schoch. Last updated 6 months ago.

fortran openblas

11.5 match 1 stars 3.70 score 8 scripts

bioc

MsCoreUtils:Core Utils for Mass Spectrometry Data

MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages.

Maintained by RforMassSpectrometry Package Maintainer. Last updated 4 days ago.

infrastructure proteomics massspectrometry metabolomics bioconductor mass-spectrometry utils

4.0 match 16 stars 10.52 score 41 scripts 71 dependents

aalfons

robmedExtra:Extra Functionality for (Robust) Mediation Analysis

This companion package extends the package 'robmed' (Alfons, Ates & Groenen, 2022b; <doi:10.18637/jss.v103.i13>) in various ways. Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>) to make the method more accessible to less proficient 'R' users, as well as functions to export the results as a table in a 'Microsoft Word' or 'Microsoft Powerpoint' document, or as a 'LaTeX' table. Furthermore, the package contains a 'shiny' app to compare various bootstrap procedures for mediation analysis on simulated data.

Maintained by Andreas Alfons. Last updated 4 months ago.

15.6 match 1 stars 2.70 score

valeriapolicastro

robin:ROBustness in Network

Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) <https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.

Maintained by Valeria Policastro. Last updated 19 days ago.

5.5 match 18 stars 7.66 score 8 scripts

tushiqi

MAnorm2:Tools for Normalizing and Comparing ChIP-seq Samples

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) <doi:10.1101/gr.262675.120> and Chen et al. (2022) <doi:10.1186/s13059-022-02627-9> for associated statistical details.

Maintained by Shiqi Tu. Last updated 2 years ago.

chip-seq differential-analysis empirical-bayes winsorize-values

7.7 match 32 stars 5.48 score 19 scripts

bioc

RPA:RPA: Robust Probabilistic Averaging for probe-level analysis

Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays.

Maintained by Leo Lahti. Last updated 5 months ago.

geneexpression microarray preprocessing qualitycontrol

7.2 match 5.78 score 20 scripts 1 dependents

fbartos

RoBTT:Robust Bayesian T-Test

An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The 'RoBTT' package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.

Maintained by František Bartoš. Last updated 4 months ago.

bayesian model-averaging outliers t-test cpp

7.9 match 3 stars 5.26 score 9 scripts

henrikbengtsson

aroma.affymetrix:Analysis of Large Affymetrix Microarray Data Sets

A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

infrastructure proprietaryplatforms exonarray microarray onechannel gui dataimport datarepresentation preprocessing qualitycontrol visualization reportwriting acgh copynumbervariants differentialexpression geneexpression snp transcription affymetrix analysis copy-number dna expression hpc large-scale notebook reproducibility rna

7.2 match 10 stars 5.79 score 112 scripts 3 dependents

alexanderrobitzsch

sirt:Supplementary Item Response Theory Models

Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).

Maintained by Alexander Robitzsch. Last updated 3 months ago.

item-response-theory openblas cpp

4.1 match 23 stars 10.01 score 280 scripts 22 dependents

valentint

rrcov3way:Robust Methods for Multiway Data Analysis, Applicable also for Compositional Data

Provides methods for multiway data analysis by means of Parafac and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).

Maintained by Valentin Todorov. Last updated 1 years ago.

9.6 match 4.28 score 38 scripts

nhejazi

txshift:Efficient Estimation of the Causal Effects of Stochastic Interventions

Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in 'sl3', available for download from GitHub using 'remotes::install_github("tlverse/sl3")'.

Maintained by Nima Hejazi. Last updated 6 months ago.

causal-effects causal-inference censored-data machine-learning robust-statistics statistics stochastic-interventions stochastic-treatment-regimes targeted-learning treatment-effects variable-importance

8.0 match 14 stars 5.12 score 19 scripts

helske

ramcmc:Robust Adaptive Metropolis Algorithm

Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. The package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.

Maintained by Jouni Helske. Last updated 3 years ago.

openblas cpp

6.5 match 6 stars 6.21 score 8 scripts 12 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 11 days ago.

4.3 match 1 stars 9.40 score 428 scripts 606 dependents

aplantin

MiRKAT:Microbiome Regression-Based Kernel Association Tests

Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). The package can also use robust regression (unpublished work) and integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information.

Maintained by Anna Plantinga. Last updated 2 years ago.

8.4 match 3 stars 4.74 score 183 scripts

bioc

GARS:GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.

Maintained by Mattia Chiesa. Last updated 5 months ago.

classification featureextraction clustering openjdk

7.9 match 5.00 score 2 scripts

s-abbas

robnptests:Robust Nonparametric Two-Sample Tests for Location/Scale

Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>. The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>. Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero.

Maintained by Sermad Abbas. Last updated 2 years ago.

8.2 match 2 stars 4.78 score 6 scripts

cwatson

brainGraph:Graph Theory Analysis of Brain MRI Data

A set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures.

Maintained by Christopher G. Watson. Last updated 1 years ago.

brain-connectivity brain-imaging complex-networks connectome connectomics fmri graph-theory mri network-analysis neuroimaging neuroscience statistics tractography

5.0 match 188 stars 7.86 score 107 scripts 3 dependents

kloke

npsm:Nonparametric Statistical Methods

Accompanies the book "Nonparametric Statistical Methods Using R, 2nd Edition" by Kloke and McKean (2024, ISBN:9780367651350). Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics. Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems. Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method.

Maintained by John Kloke. Last updated 9 months ago.

11.2 match 3.47 score 59 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.

openjdk

7.2 match 5 stars 5.37 score 93 scripts

haotianxu

changepoints:A Collection of Change-Point Detection Methods

Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.

Maintained by Haotian Xu. Last updated 1 years ago.

openblas cpp

6.7 match 12 stars 5.78 score 25 scripts

cran

rdrobust:Robust Data-Driven Statistical Inference in Regression-Discontinuity Designs

Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).

Maintained by Sebastian Calonico. Last updated 1 years ago.

6.7 match 4 stars 5.70 score 638 scripts 6 dependents

fchamroukhi

meteorits:Mixture-of-Experts Modeling for Complex Non-Normal Distributions

Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F. (2015) <arXiv:1506.06707>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>. Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>. Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence clustering em-algorithm mixture-of-experts neural-networks non-linear-regression prediction robust-learning skew-normal skew-t skewed-data statistical-inference statistical-learning t-distribution unsupervised-learning openblas cpp

7.5 match 3 stars 5.12 score 11 scripts

r-lib

lintr:A 'Linter' for R Code

Checks adherence to a given style, syntax errors and possible semantic issues. Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text', 'Atom' and 'Visual Studio Code'.

Maintained by Michael Chirico. Last updated 8 days ago.

linter

2.3 match 1.2k stars 17.00 score 916 scripts 33 dependents

cran

ssmrob:Robust Estimation and Inference in Sample Selection Models

Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) <doi:10.18637/jss.v099.i04>.

Maintained by Mikhail Zhelonkin. Last updated 4 years ago.

38.2 match 1.00 score 9 scripts

jaak-s

rDEA:Robust Data Envelopment Analysis (DEA) for R

Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.

Maintained by Jaak Simm. Last updated 2 years ago.

glpk

7.7 match 24 stars 4.92 score 23 scripts

cran

robfilter:Robust Time Series Filters

Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> and the slope comparing adaptive repeated median by Borowski and Fried (2013) <doi:10.1007/s11222-013-9391-7> choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393> for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.

Maintained by Roland Fried. Last updated 8 months ago.

cpp

19.9 match 2 stars 1.90 score

christiangoueguel

ConfidenceEllipse:Computation of 2D and 3D Elliptical Joint Confidence Regions

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.

Maintained by Christian L. Goueguel. Last updated 11 months ago.

confidence-ellipse confidence-ellipsoid confidence-region multivariate-distribution outliers-detection robust-statistics

8.0 match 1 stars 4.70 score

hsnbulut

MVTests:Multivariate Hypothesis Tests

Multivariate hypothesis tests and the confidence intervals. It can be used to test the hypothesizes about mean vector or vectors (one-sample, two independent samples, paired samples), covariance matrix (one or more matrices), and the correlation matrix. Moreover, it can be used for robust Hotelling T^2 test at one sample case in high dimensional data. For this package, we have benefited from the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.

Maintained by Hasan Bulut. Last updated 5 months ago.

11.2 match 3.30 score 40 scripts

kolesarm

dfadjust:Degrees of Freedom Adjustment for Robust Standard Errors

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.

Maintained by Michal Kolesár. Last updated 3 months ago.

6.4 match 31 stars 5.75 score 12 scripts

konfound-project

konfound:Quantify the Robustness of Causal Inferences

Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.

Maintained by Joshua M Rosenberg. Last updated 11 days ago.

5.2 match 15 stars 7.07 score 42 scripts

bioc

flowClust:Clustering for Flow Cytometry

Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'.

Maintained by Greg Finak. Last updated 5 months ago.

immunooncology clustering visualization flowcytometry

5.0 match 7.30 score 83 scripts 6 dependents

cran

adamethods:Archetypoid Algorithms and Anomaly Detection

Collection of several algorithms to obtain archetypoids with small and large databases, and with both classical multivariate data and functional data (univariate and multivariate). Some of these algorithms also allow to detect anomalies (outliers). Please see Vinue and Epifanio (2020) <doi:10.1007/s11634-020-00412-9>.

Maintained by Guillermo Vinue. Last updated 5 years ago.

22.2 match 1.63 score 43 scripts

subroy13

rsvddpd:Robust Singular Value Decomposition using Density Power Divergence

Computing singular value decomposition with robustness is a challenging task. This package provides an implementation of computing robust SVD using density power divergence (<arXiv:2109.10680>). It combines the idea of robustness and efficiency in estimation based on a tuning parameter. It also provides utility functions to simulate various scenarios to compare performances of different algorithms.

Maintained by Subhrajyoty Roy. Last updated 2 years ago.

openblas cpp openmp

8.7 match 3 stars 4.18 score 6 scripts

antoinelucas64

amap:Another Multidimensional Analysis Package

Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions).

Maintained by Antoine Lucas. Last updated 5 months ago.

fortran cpp

4.7 match 7.66 score 460 scripts 26 dependents

bioc

LimROTS:A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Analysis of Proteomics and Metabolomics Data

Differential expression analysis is a prevalent method utilised in the examination of diverse biological data. The reproducibility-optimized test statistic (ROTS) modifies a t-statistic based on the data's intrinsic characteristics and ranks features according to their statistical significance for differential expression between two or more groups (f-statistic). Focussing on proteomics and metabolomics, the current ROTS implementation cannot account for technical or biological covariates such as MS batches or gender differences among the samples. Consequently, we developed LimROTS, which employs a reproducibility-optimized test statistic utilising the limma methodology to simulate complex experimental designs. LimROTS is a hybrid method integrating empirical bayes and reproducibility-optimized statistics for robust analysis of proteomics and metabolomics data.

Maintained by Ali Mostafa Anwar. Last updated 3 months ago.

software geneexpression differentialexpression microarray rnaseq proteomics immunooncology metabolomics mrnamicroarray

7.5 match 1 stars 4.70 score 1 scripts

cran

bst:Gradient Boosting

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Maintained by Zhu Wang. Last updated 2 years ago.

8.3 match 4.17 score 5 dependents

ericaponzi

RaJIVE:Robust Angle Based Joint and Individual Variation Explained

A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

Maintained by Erica Ponzi. Last updated 4 years ago.

12.8 match 2.70 score 1 scripts

yunyishen

robustcov:Collection of Robust Covariance and (Sparse) Precision Matrix Estimators

Collection of methods for robust covariance and (sparse) precision matrix estimation based on Loh and Tan (2018) <doi:10.1214/18-EJS1427>.

Maintained by Yunyi Shen. Last updated 4 years ago.

precision-matrix robust-estimates openblas cpp openmp

12.8 match 1 stars 2.70 score

jamovi

walrus:Robust Statistical Methods

A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for 'jamovi' (see <https://www.jamovi.org> for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book 'Introduction to Robust Estimation & Hypothesis Testing'.

Maintained by Jonathon Love. Last updated 2 years ago.

jamovi

12.5 match 2 stars 2.68 score 12 scripts

braverock

PortfolioAnalytics:Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

Portfolio optimization and analysis routines and graphics.

Maintained by Brian G. Peterson. Last updated 3 months ago.

2.9 match 81 stars 11.49 score 626 scripts 2 dependents

bioc

GENESIS:GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness

The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.

Maintained by Stephanie M. Gogarten. Last updated 1 months ago.

snp geneticvariability genetics statisticalmethod dimensionreduction principalcomponent genomewideassociation qualitycontrol biocviews

3.2 match 36 stars 10.44 score 342 scripts 1 dependents

bioc

AlpsNMR:Automated spectraL Processing System for NMR

Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available.

Maintained by Sergio Oller Moreno. Last updated 5 months ago.

software preprocessing visualization classification cheminformatics metabolomics dataimport

4.4 match 15 stars 7.59 score 12 scripts 1 dependents

pecanproject

PEcAn.utils:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Rob Kooper. Last updated 2 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants

3.0 match 216 stars 10.92 score 218 scripts 35 dependents

cran

metaplus:Robust Meta-Analysis and Meta-Regression

Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

Maintained by Ken Beath. Last updated 2 months ago.

8.2 match 3.96 score 34 scripts

kolesarm

ebci:Robust Empirical Bayes Confidence Intervals

Computes empirical Bayes confidence estimators and confidence intervals in a normal means model. The intervals are robust in the sense that they achieve correct coverage regardless of the distribution of the means. If the means are treated as fixed, the intervals have an average coverage guarantee. The implementation is based on Armstrong, Kolesár and Plagborg-Møller (2022) <doi:10.3982/ECTA18597>.

Maintained by Michal Kolesár. Last updated 7 months ago.

6.5 match 10 stars 5.00 score 3 scripts

aadler

revss:Robust Estimation in Very Small Samples

Implements the estimation techniques described in Rousseeuw & Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the location and scale of very small samples.

Maintained by Avraham Adler. Last updated 9 months ago.

9.0 match 4 stars 3.60 score

bioc

marray:Exploratory analysis for two-color spotted microarray data

Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking.

Maintained by Yee Hwa (Jean) Yang. Last updated 5 months ago.

microarray twochannel preprocessing

3.6 match 8.92 score 222 scripts 37 dependents

leelabsg

SKAT:SNP-Set (Sequence) Kernel Association Test

Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.

Maintained by Seunggeun (Shawn) Lee. Last updated 1 months ago.

sequence cpp

3.3 match 45 stars 9.70 score 268 scripts 16 dependents

drjoze

drgee:Doubly Robust Generalized Estimating Equations

Fit restricted mean models for the conditional association between an exposure and an outcome, given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.

Maintained by Johan Zetterqvist. Last updated 5 years ago.

openblas cpp

9.1 match 3.50 score 35 scripts 3 dependents

andreasnordland

polle:Policy Learning

Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust learning of the blip/conditional average treatment effect and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.

Maintained by Andreas Nordland. Last updated 9 days ago.

5.5 match 4 stars 5.76 score 6 scripts

cran

SSDforR:Functions to Analyze Single System Data

Functions to visually and statistically analyze single system data.

Maintained by Charles Auerbach. Last updated 3 months ago.

12.9 match 2.44 score 80 scripts

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 3 months ago.

2.3 match 52 stars 13.94 score 29k scripts 317 dependents

mbannick

RobinCar:Robust Inference for Covariate Adjustment in Randomized Clinical Trials

Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials. Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>. Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>. Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>. Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.48550/arXiv.2306.10213>.

Maintained by Marlena Bannick. Last updated 6 days ago.

7.0 match 6 stars 4.42 score 11 scripts

rogih

acfMPeriod:Robust Estimation of the ACF from the M-Periodogram

Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) <doi:10.1002/9780470316917>. The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.

Maintained by Higor Cotta. Last updated 6 years ago.

15.4 match 2.00 score

russellpierce

naptime:A Flexible and Robust Sys.sleep() Replacement

Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.

Maintained by Russell S. Pierce. Last updated 7 months ago.

5.9 match 9 stars 5.21 score 12 scripts 1 dependents

unina-sfere

rofanova:Robust Functional Analysis of Variance

Implements the robust functional analysis of variance (RoFANOVA), described in Centofanti et al. (2021) <arXiv:2112.10643>. It allows testing mean differences among groups of functional data by being robust against the presence of outliers.

Maintained by Fabio Centofanti. Last updated 3 years ago.

openblas cpp openmp

9.5 match 3.22 score 11 scripts 1 dependents

shabbychef

fromo:Fast Robust Moments

Fast, numerically robust computation of weighted moments via 'Rcpp'. Supports computation on vectors and matrices, and Monoidal append of moments. Moments and cumulants over running fixed length windows can be computed, as well as over time-based windows. Moment computations are via a generalization of Welford's method, as described by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.

Maintained by Steven E. Pav. Last updated 4 months ago.

cumulants moments rolling-statistics statistics cpp

5.8 match 3 stars 5.22 score 22 scripts

xmengju

RRBoost:A Robust Boosting Algorithm

An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <10.1016/j.csda.2007.11.006>).

Maintained by Xiaomeng Ju. Last updated 4 months ago.

11.3 match 2.70 score 3 scripts

lbb220

GWmodel:Geographically-Weighted Models

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

Maintained by Binbin Lu. Last updated 6 months ago.

openblas cpp openmp

4.8 match 18 stars 6.38 score 266 scripts 4 dependents

quantumofmoose

complexlm:Linear Fitting for Complex Valued Data

Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.

Maintained by William Ryan. Last updated 1 years ago.

complex-numbers fitting linear-models linear-regression robust-statistics statistics

15.1 match 1 stars 2.00 score 6 scripts

bioc

ClustAll:ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases

Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types).

Maintained by Asier Ortega-Legarreta. Last updated 5 months ago.

software statisticalmethod clustering dimensionreduction principalcomponent

8.0 match 3.78 score 1 scripts

paulsmirnov

robcor:Robust Correlations

Robust pairwise correlations based on estimates of scale, particularly on "FastQn" one-step M-estimate.

Maintained by Paul Smirnov. Last updated 3 years ago.

11.6 match 2.58 score 21 scripts 6 dependents

avehtari

aaltobda:Functionality and Data for the Aalto Course on Bayesian Data Analysis

Functionality and Data for the Aalto University Course on Bayesian Data Analysis.

Maintained by Aki Vehtari. Last updated 3 months ago.

bayes bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-workflow

3.4 match 2.2k stars 8.93 score 159 scripts

fvidoli

Compind:Composite Indicators Functions

A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.

Maintained by Francesco Vidoli. Last updated 2 months ago.

10.3 match 1 stars 2.90 score 40 scripts

bioc

netSmooth:Network smoothing for scRNAseq

netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data.

Maintained by Jonathan Ronen. Last updated 5 months ago.

network graphandnetwork singlecell rnaseq geneexpression sequencing transcriptomics normalization preprocessing clustering dimensionreduction bioinformatics genomics single-cell

4.0 match 27 stars 7.41 score 4 scripts

fbertran

robustfa:Object Oriented Solution for Robust Factor Analysis

Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) <doi:10.18637/jss.v032.i03>), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes.

Maintained by Frederic Bertrand. Last updated 2 years ago.

12.3 match 2.40 score 25 scripts

wahani

saeRobust:Robust Small Area Estimation

Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.

Maintained by Sebastian Warnholz. Last updated 1 years ago.

openblas cpp

7.3 match 1 stars 4.03 score 12 scripts 3 dependents

muschellij2

fslr:Wrapper Functions for 'FSL' ('FMRIB' Software Library) from Functional MRI of the Brain ('FMRIB')

Wrapper functions that interface with 'FSL' <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging' software, using system commands. The goal is to be able to interface with 'FSL' completely in R, where you pass R objects of class 'nifti', implemented by package 'oro.nifti', and the function executes an 'FSL' command and returns an R object of class 'nifti' if desired.

Maintained by John Muschelli. Last updated 1 months ago.

fsl fslr neuroimaging neuroimaging-analysis neuroimaging-data-science

3.7 match 41 stars 8.01 score 420 scripts

laylaparast

Rsurrogate:Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information

Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.

Maintained by Layla Parast. Last updated 2 years ago.

9.3 match 3.16 score 12 scripts 4 dependents

shanascogin

coxrobust:Fit Robustly Proportional Hazards Regression Model

An implementation of robust estimation in Cox model. Functionality includes fitting efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject. Method is based on a smooth modification of the partial likelihood.

Maintained by Shana Scogin. Last updated 3 years ago.

7.2 match 3 stars 4.05 score 21 scripts 2 dependents

amishra-stats

robregcc:Robust Regression with Compositional Covariates

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

Maintained by Aditya Mishra. Last updated 4 years ago.

openblas cpp

7.1 match 6 stars 4.11 score 43 scripts

bioc

epimutacions:Robust outlier identification for DNA methylation data

The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

dnamethylation biologicalquestion preprocessing statisticalmethod normalization cpp

6.8 match 4.23 score 28 scripts

dddlab

robsel:Robust Selection Algorithm

An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.

Maintained by Chau Tran. Last updated 4 years ago.

cpp

6.8 match 2 stars 4.28 score 19 scripts

dakep

pyinit:Pena-Yohai Initial Estimator for Robust S-Regression

Deterministic Pena-Yohai initial estimator for robust S estimators of regression. The procedure is described in detail in Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.

Maintained by David Kepplinger. Last updated 3 years ago.

openblas

5.3 match 1 stars 5.43 score 17 scripts 9 dependents

rfastofficial

Rfast2:A Collection of Efficient and Extremely Fast R Functions II

A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

Maintained by Manos Papadakis. Last updated 1 years ago.

openblas cpp openmp

3.5 match 38 stars 8.09 score 75 scripts 26 dependents

bioc

rbsurv:Robust likelihood-based survival modeling with microarray data

This package selects genes associated with survival.

Maintained by Soo-heang Eo. Last updated 5 months ago.

microarray

6.5 match 4.30 score 7 scripts

reckziegel

epo:Enhanced Portfolio Optimization (EPO)

Implements the Enhanced Portfolio Optimization (EPO) method as described in Pedersen, Babu and Levine (2021) <doi:10.2139/ssrn.3530390>.

Maintained by Bernardo Reckziegel. Last updated 1 years ago.

bayesian-optimization black-litterman mean-variance-optimization principal-component-analysis robust-optimization

7.5 match 10 stars 3.70 score 4 scripts

cran

rlcv:Robust Likelihood Cross Validation Bandwidth Selection

Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.

Maintained by Ximing Wu. Last updated 3 years ago.

10.3 match 2.70 score

salvatoremangiafico

rcompanion:Functions to Support Extension Education Program Evaluation

Functions and datasets to support Summary and Analysis of Extension Program Evaluation in R, and An R Companion for the Handbook of Biological Statistics. Vignettes are available at <https://rcompanion.org>.

Maintained by Salvatore Mangiafico. Last updated 30 days ago.

3.4 match 4 stars 8.01 score 2.4k scripts 5 dependents

bioc

sccomp:Tests differences in cell-type proportion for single-cell data, robust to outliers

A robust and outlier-aware method for testing differences in cell-type proportion in single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.

Maintained by Stefano Mangiola. Last updated 16 days ago.

bayesian regression differentialexpression singlecell batch-correction composition cytof differential-proportion microbiome multilevel proportions random-effects single-cell unwanted-variation

3.3 match 99 stars 8.41 score 69 scripts

emilopezcano

SixSigma:Six Sigma Tools for Quality Control and Improvement

Functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package.

Maintained by Emilio L. Cano. Last updated 2 years ago.

quality-control quality-improvement six-sigma spc

3.5 match 15 stars 7.82 score 169 scripts 1 dependents

ewouddt

RcmdrPlugin.BiclustGUI:'Rcmdr' Plug-in GUI for Biclustering

A plug-in for R Commander ('Rcmdr'). The package is a Graphical User Interface (GUI) in which several biclustering methods can be executed, followed by diagnostics and plots of the results. Further, the GUI also has the possibility to connect the methods to more general diagnostic packages for biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG', 'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and 'BcDiag' are also implemented to be able to further investigate results. The GUI also provides a couple of extra utilities to export, save, search through and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific framework for biclustering in which new methods, diagnostics and plots can be added. Scripts were prepared so that R-package developers can freely design their own dialogs in the GUI which can then be added by the maintainer of 'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk' and 'Rcmdr' and are easy to fill in. (Note: rqubic currently requires manual installation through BiocManager::install('rqubic').)

Maintained by De Troyer Ewoud. Last updated 3 years ago.

10.1 match 2.70 score 1 scripts

enricoschumann

NMOF:Numerical Methods and Optimization in Finance

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

Maintained by Enrico Schumann. Last updated 30 days ago.

black-scholes differential-evolution genetic-algorithm grid-search heuristics implied-volatility local-search optimization particle-swarm-optimization simulated-annealing threshold-accepting

2.9 match 36 stars 9.56 score 101 scripts 4 dependents

alexisderumigny

MMDCopula:Robust Estimation of Copulas by Maximum Mean Discrepancy

Provides functions for the robust estimation of parametric families of copulas using minimization of the Maximum Mean Discrepancy, following the article Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

Maintained by Alexis Derumigny. Last updated 3 years ago.

copula-models copulas mmd r-pkg

6.2 match 5 stars 4.40 score 3 scripts

bioc

dearseq:Differential Expression Analysis for RNA-seq data through a robust variance component test

Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.

Maintained by Boris P. Hejblum. Last updated 5 months ago.

biomedicalinformatics cellbiology differentialexpression dnaseq geneexpression genetics genesetenrichment immunooncology kegg regression rnaseq sequencing systemsbiology timecourse transcription transcriptomics

4.4 match 8 stars 6.20 score 11 scripts 1 dependents

bioc

DESeq2:Differential gene expression analysis based on the negative binomial distribution

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Maintained by Michael Love. Last updated 11 days ago.

sequencing rnaseq chipseq geneexpression transcription normalization differentialexpression bayesian regression principalcomponent clustering immunooncology openblas cpp

1.7 match 375 stars 16.11 score 17k scripts 115 dependents

harveyklyne

drape:Doubly Robust Average Partial Effects

Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.

Maintained by Harvey Klyne. Last updated 4 months ago.

6.7 match 2 stars 4.00 score 4 scripts

zhuwang46

irboost:Iteratively Reweighted Boosting for Robust Analysis

Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.

Maintained by Zhu Wang. Last updated 1 months ago.

8.9 match 3.00 score

jdgonzalezwork

ktaucenters:Robust Clustering Procedures

A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).

Maintained by Juan Domingo Gonzalez. Last updated 1 years ago.

cpp

8.3 match 3.18 score 5 scripts 1 dependents

chedgala

lqr:Robust Linear Quantile Regression

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

Maintained by Christian E. Galarza. Last updated 8 months ago.

12.6 match 1 stars 2.08 score 9 scripts 2 dependents

stopsack

risks:Estimate Risk Ratios and Risk Differences using Regression

Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, <doi:10.1093/aje/kwi188>), and others.

Maintained by Konrad Stopsack. Last updated 11 months ago.

binomial biostatistics epidemiology regression-models

5.3 match 5 stars 4.95 score 12 scripts

uncertaintyquantification

RobustCalibration:Robust Calibration of Imperfect Mathematical Models

Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics.

Maintained by Mengyang Gu. Last updated 10 months ago.

cpp

21.3 match 1.23 score 17 scripts

bioc

openCyto:Hierarchical Gating Pipeline for flow cytometry data

This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy.

Maintained by Mike Jiang. Last updated 5 months ago.

immunooncology flowcytometry dataimport preprocessing datarepresentation cpp

3.4 match 7.62 score 404 scripts 1 dependents

xiaooupan

FarmTest:Factor-Adjusted Robust Multiple Testing

Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>. Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.

Maintained by Xiaoou Pan. Last updated 4 years ago.

openblas cpp openmp

7.5 match 4 stars 3.48 score 15 scripts

aalfons

laeken:Estimation of Indicators on Social Exclusion and Poverty

Estimation of indicators on social exclusion and poverty, as well as Pareto tail modeling for empirical income distributions.

Maintained by Andreas Alfons. Last updated 1 years ago.

2.7 match 3 stars 9.57 score 300 scripts 30 dependents

bioc

TCC:TCC: Differential expression analysis for tag count data with robust normalization strategies

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.

Maintained by Jianqiang Sun. Last updated 5 months ago.

immunooncology sequencing differentialexpression rnaseq

5.2 match 4.91 score 41 scripts

kenaho1

asbio:A Collection of Statistical Tools for Biologists

Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.

Maintained by Ken Aho. Last updated 2 months ago.

3.5 match 5 stars 7.32 score 310 scripts 3 dependents

mandymejia

fMRIscrub:Scrubbing and Other Data Cleaning Routines for fMRI

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

Maintained by Amanda Mejia. Last updated 2 years ago.

5.5 match 4 stars 4.56 score 15 scripts 1 dependents