R-universe search: adjusted

robindenz1

adjustedCurves:Confounder-Adjusted Survival Curves and Cumulative Incidence Functions

Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.

Maintained by Robin Denz. Last updated 29 days ago.

adjusted confidence-intervals cumulative-incidence survival-curves

69.8 match 38 stars 8.12 score 93 scripts

christophsax

seasonal:R Interface to X-13-ARIMA-SEATS

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package.

Maintained by Christoph Sax. Last updated 17 days ago.

seasonal-adjustment time-series

27.2 match 120 stars 12.03 score 1.1k scripts 8 dependents

finnishcancerregistry

popEpi:Functions for Epidemiological Analysis using Population Data

Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'.

Maintained by Joonas Miettinen. Last updated 2 months ago.

adjust-estimates age-adjusting direct-adjusting epidemiology indirect-adjusting survival

39.0 match 8 stars 8.05 score 117 scripts 1 dependents

ikosmidis

brglm2:Bias Reduction in Generalized Linear Models

Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).

Maintained by Ioannis Kosmidis. Last updated 6 months ago.

adjusted-score-equations algorithms bias-reducing-adjustments bias-reduction estimation glm logistic-regression nominal-responses ordinal-responses regression regression-algorithms statistics

21.6 match 32 stars 10.41 score 106 scripts 10 dependents

rvlenth

emmeans:Estimated Marginal Means, aka Least-Squares Means

Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.

Maintained by Russell V. Lenth. Last updated 4 days ago.

10.5 match 377 stars 19.19 score 13k scripts 187 dependents

dcousin3

superb:Summary Plots with Adjusted Error Bars

Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references.

Maintained by Denis Cousineau. Last updated 2 months ago.

error-bars plotting statistics summary-plots summary-statistics visualization

20.9 match 19 stars 9.55 score 155 scripts 2 dependents

jbengler

tidyplots:Tidy Plots for Scientific Papers

The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.

Maintained by Jan Broder Engler. Last updated 4 days ago.

19.5 match 482 stars 9.40 score 85 scripts

allenzhuaz

MHTdiscrete:Multiple Hypotheses Testing for Discrete Data

A comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling FWER/FDR for discrete data. Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users' convenience, the functions also provide the output option for printing decision rules.

Maintained by Yalin Zhu. Last updated 6 years ago.

adjustment-computations benjamini-hochberg bonferroni discrete-distributions multiple-testing-correction

55.8 match 1 stars 3.27 score 37 scripts

rjdverse

RJDemetra:Interface to 'JDemetra+' Seasonal Adjustment Software

Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks. It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.

Maintained by Alain Quartier-la-Tente. Last updated 10 days ago.

jdemetra openjdk

19.5 match 53 stars 8.67 score 128 scripts 5 dependents

bioc

gcrma:Background Adjustment Using Sequence Information

Background adjustment using sequence information

Maintained by Z. Wu. Last updated 5 months ago.

microarray onechannel preprocessing

22.7 match 7.28 score 164 scripts 11 dependents

poissonconsulting

extras:Helper Functions for Bayesian Analyses

Functions to 'numericise' 'R' objects (coerce to numeric objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as 'R' translations of some 'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.

Maintained by Nicole Hill. Last updated 2 months ago.

19.0 match 9 stars 8.49 score 15 scripts 16 dependents

pletschm

aldvmm:Adjusted Limited Dependent Variable Mixture Models

The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

Maintained by Mark Pletscher. Last updated 1 years ago.

clinical-trials cost-effectiveness eq5d finite-mixture health-economics hta hui limited-dependent-variable mapping mixture-model patient-reported-outcomes quality-of-life utilities

35.0 match 5 stars 4.40 score 2 scripts

talgalili

dendextend:Extending 'dendrogram' Functionality in R

Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.

Maintained by Tal Galili. Last updated 2 months ago.

8.4 match 154 stars 17.02 score 6.0k scripts 164 dependents

gamlss-dev

gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape

A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.

Maintained by Mikis Stasinopoulos. Last updated 22 days ago.

13.5 match 4 stars 10.50 score 346 scripts 71 dependents

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

32.0 match 3 stars 4.29 score 13 scripts

distancedevelopment

mrds:Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Maintained by Laura Marshall. Last updated 2 months ago.

15.7 match 4 stars 8.05 score 78 scripts 7 dependents

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 7 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

18.2 match 5 stars 6.64 score 29 scripts

dankelley

oce:Analysis of Oceanographic Data

Supports the analysis of Oceanographic data, including 'ADCP' measurements, measurements made with 'argo' floats, 'CTD' measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.

Maintained by Dan Kelley. Last updated 2 days ago.

oceanography fortran cpp

7.3 match 146 stars 15.42 score 4.2k scripts 18 dependents

rsquaredacademy

olsrr:Tools for Building OLS Regression Models

Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.

Maintained by Aravind Hebbali. Last updated 4 months ago.

collinearity-diagnostics linear-models regression stepwise-regression

9.1 match 103 stars 12.19 score 1.4k scripts 4 dependents

tidyverse

ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Maintained by Thomas Lin Pedersen. Last updated 10 days ago.

data-visualisation visualisation

4.3 match 6.6k stars 25.10 score 645k scripts 7.5k dependents

bschneidr

svrep:Tools for Creating, Updating, and Analyzing Survey Replicate Weights

Provides tools for creating and working with survey replicate weights, extending functionality of the 'survey' package from Lumley (2004) <doi:10.18637/jss.v009.i08>. Implements bootstrap methods for complex surveys, including the generalized survey bootstrap as described by Beaumont and Patak (2012) <doi:10.1111/j.1751-5823.2011.00166.x>. Methods are provided for applying nonresponse adjustments to both full-sample and replicate weights as described by Rust and Rao (1996) <doi:10.1177/096228029600500305>. Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>. Diagnostic functions are included to compare weights and weighted estimates from different sets of replicate weights.

Maintained by Ben Schneider. Last updated 7 days ago.

12.5 match 8 stars 8.12 score 54 scripts 3 dependents

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 6 hours ago.

7.1 match 845 stars 13.60 score 264 scripts 2 dependents

bioc

ComplexHeatmap:Make Complex Heatmaps

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics.

Maintained by Zuguang Gu. Last updated 5 months ago.

software visualization sequencing clustering complex-heatmaps heatmap

5.7 match 1.3k stars 16.93 score 16k scripts 151 dependents

mlverse

torchvision:Models, Datasets and Transformations for Images

Provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the 'torch' package and it's 'API' borrows heavily from 'PyTorch' vision package.

Maintained by Daniel Falbel. Last updated 6 months ago.

9.8 match 65 stars 9.74 score 313 scripts 6 dependents

davisvaughan

almanac:Tools for Working with Recurrence Rules

Provides tools for defining recurrence rules and recurrence sets. Recurrence rules are a programmatic way to define a recurring event, like the first Monday of December. Multiple recurrence rules can be combined into larger recurrence sets. A full holiday and calendar interface is also provided that can generate holidays within a particular year, can detect if a date is a holiday, can respect holiday observance rules, and allows for custom holidays.

Maintained by Davis Vaughan. Last updated 2 years ago.

calendars holidays recurrence-rules

11.1 match 73 stars 8.40 score 65 scripts 1 dependents

jtextor

dagitty:Graphical Analysis of Structural Causal Models

A port of the web-based software 'DAGitty', available at <https://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.

Maintained by Johannes Textor. Last updated 3 months ago.

7.1 match 302 stars 12.83 score 1.7k scripts 11 dependents

strengejacke

ggeffects:Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.

Maintained by Daniel Lüdecke. Last updated 6 days ago.

estimated-marginal-means hacktoberfest marginal-effects prediction

5.9 match 588 stars 15.55 score 3.6k scripts 7 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 7 days ago.

19.9 match 6 stars 4.42 score 11 scripts

soodoku

guess:Adjust Estimates of Learning for Guessing

Adjust Estimates of Learning for Guessing. The package provides standard guessing correction, and a latent class model that leverages informative pre-post transitions. For details of the latent class model, see <http://gsood.com/research/papers/guess.pdf>.

Maintained by Gaurav Sood. Last updated 3 years ago.

adjust-estimates bias learning

20.5 match 3 stars 4.29 score 13 scripts

cran

cusum:Cumulative Sum (CUSUM) Charts for Monitoring of Hospital Performance

Provides functions for constructing and evaluating CUSUM charts and RA-CUSUM charts with focus on false signal probability.

Maintained by Lena Hubig. Last updated 5 years ago.

cpp

22.6 match 3.81 score 16 scripts

bioc

BERT:High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT)

Provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees.

Maintained by Yannis Schumann. Last updated 2 months ago.

batcheffect preprocessing experimentaldesign qualitycontrol batch-effect bioconductor-package bioinformatics data-integration data-science

15.9 match 2 stars 5.40 score 18 scripts

pik-piam

mrtransport:Input data generation for the EDGE-Transport model

The mrtransport package contains data preprocessing for the EDGE-Transport model.

Maintained by Johanna Hoppe. Last updated 7 days ago.

17.5 match 4.84 score 3 dependents

kaifenglu

lrstat:Power and Sample Size Calculation for Non-Proportional Hazards and Beyond

Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.

Maintained by Kaifeng Lu. Last updated 3 months ago.

cpp

15.1 match 2 stars 5.58 score 30 scripts

wilsonfreitas

bizdays:Business Days Calculations and Utilities

Business days calculations based on a list of holidays and nonworking weekdays. Quite useful for fixed income and derivatives pricing.

Maintained by Wilson Freitas. Last updated 2 months ago.

bizdays business-days calendar holidays market-calendar

8.5 match 55 stars 9.89 score 426 scripts 3 dependents

christophsax

seasonalview:Graphical User Interface for Seasonal Adjustment

A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software.

Maintained by Christoph Sax. Last updated 5 months ago.

seasonal-adjustment shiny time-series

14.6 match 22 stars 5.59 score 105 scripts

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.

5.1 match 222 stars 15.93 score 4.8k scripts 20 dependents

insightsengineering

chevron:Standard TLGs for Clinical Trials Reporting

Provide standard tables, listings, and graphs (TLGs) libraries used in clinical trials. This package implements a structure to reformat the data with 'dunlin', create reporting tables using 'rtables' and 'tern' with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality.

Maintained by Joe Zhu. Last updated 25 days ago.

clinical-trials graphs listings nest reporting tables

9.8 match 12 stars 8.24 score 12 scripts

ewenharrison

finalfit:Quickly Create Elegant Regression Results Tables and Plots when Modelling

Generate regression results tables and plots in final format for publication. Explore models and export directly to PDF and 'Word' using 'RMarkdown'.

Maintained by Ewen Harrison. Last updated 7 months ago.

7.0 match 270 stars 11.43 score 1.0k scripts

ellisp

ggseas:'stats' for Seasonal Adjustment on the Fly with 'ggplot2'

Provides 'ggplot2' 'stats' that estimate seasonally adjusted series and rolling summaries such as rolling average on the fly for time series.

Maintained by Peter Ellis. Last updated 7 years ago.

11.9 match 74 stars 6.68 score 129 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

13.5 match 4 stars 5.88 score 18 scripts 7 dependents

merck

metalite.ae:Adverse Events Analysis Using 'metalite'

Analyzes adverse events in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

Maintained by Yujie Zhao. Last updated 1 months ago.

cdisc clinical-trials metadata

8.4 match 18 stars 9.45 score 31 scripts 2 dependents

r4ss

r4ss:R Code for Stock Synthesis

A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.23.1, from December 2024). Support for 3.24 models is only through the core functions for reading output and plotting.

Maintained by Ian G. Taylor. Last updated 5 days ago.

fisheries fisheries-stock-assessment stock-synthesis

6.9 match 43 stars 11.38 score 1.0k scripts 2 dependents

r-causal

tipr:Tipping Point Analyses

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.

Maintained by Lucy DAgostino McGowan. Last updated 1 years ago.

15.4 match 35 stars 5.02 score 60 scripts

rjdverse

rjd3toolkit:Utility Functions around 'JDemetra+ 3.0'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, UCARIMA models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats.

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

jdemetra seasonal-adjustment timeseries openjdk

13.1 match 5 stars 5.81 score 48 scripts 15 dependents

maebruck

chantrics:Loglikelihood Adjustments for Econometric Models

Adjusts the loglikelihood of common econometric models for clustered data based on the estimation process suggested in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, using the 'chandwich' package <https://cran.r-project.org/package=chandwich>, and provides convenience functions for inference on the adjusted models.

Maintained by Theo Bruckbauer. Last updated 3 years ago.

clustering econometrics likelihood likelihood-ratio-test loglikelihood-adjustment maximum-likelihood

20.4 match 3.70 score 4 scripts

timginker

boiwsa:Seasonal Adjustment of Weekly Data

Perform seasonal adjustment of weekly data. The package provides a user-friendly interface for computing seasonally adjusted estimates of weekly data and includes functions for the creation of country-specific prior adjustment variables, as well as diagnostic tools to assess the quality of the adjustments. The method is described in more detail in Ginker (2023) <doi:10.13140/RG.2.2.12221.44000>.

Maintained by Tim Ginker. Last updated 1 months ago.

seasonal-adjustment seasonality time-series-analysis

16.9 match 4 stars 4.48 score 3 scripts

r-forge

pcalg:Methods for Graphical Models and Causal Inference

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Maintained by Markus Kalisch. Last updated 6 months ago.

openblas cpp

10.2 match 7.32 score 700 scripts 19 dependents

easystats

datawizard:Easy Data Wrangling and Statistical Transformations

A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.

Maintained by Etienne Bacher. Last updated 10 days ago.

data dplyr hacktoberfest janitor manipulation reshape tidyr wrangling

5.1 match 222 stars 14.71 score 436 scripts 119 dependents

pecanproject

PEcAn.assim.batch: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 Istem Fer. Last updated 3 days ago.

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

7.4 match 216 stars 9.94 score 20 scripts 2 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

5.5 match 13.33 score 1.7k scripts 480 dependents

haeran-cho

fnets:Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arXiv:2301.11675> accompanying the R package.

Maintained by Haeran Cho. Last updated 4 months ago.

factor-models forecasting high-dimensional network-estimation time-series vector-autoregression cpp

13.6 match 7 stars 5.33 score 28 scripts

r-causal

ggdag:Analyze and Create Elegant Directed Acyclic Graphs

Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool (<https://dagitty.net/>) for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.

Maintained by Malcolm Barrett. Last updated 8 months ago.

causal-inference dag ggplot-extension

6.1 match 443 stars 11.78 score 1.8k scripts 5 dependents

acorg

Racmacs:Antigenic Cartography Macros

A toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information.

Maintained by Sam Wilks. Last updated 9 months ago.

openblas cpp openmp

8.9 match 21 stars 8.06 score 362 scripts

cefet-rj-dal

daltoolbox:Leveraging Experiment Lines to Data Analytics

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Maintained by Eduardo Ogasawara. Last updated 1 months ago.

10.8 match 1 stars 6.65 score 536 scripts 4 dependents

jchiquet

aricode:Efficient Computations of Standard Clustering Comparison Measures

Implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. Available measures include adjusted Rand index (ARI), normalized information distance (NID), normalized mutual information (NMI), adjusted mutual information (AMI), normalized variation information (NVI) and entropy, as described in Vinh et al (2009) <doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2, a modified version of ARI (MARI), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7> and simple Chi-square distance since version 1.0.0.

Maintained by Julien Chiquet. Last updated 1 years ago.

bucket-sort clustering clustering-comparison-measures cpp

8.6 match 25 stars 8.15 score 542 scripts 14 dependents

allenzhuaz

MHTmult:Multiple Hypotheses Testing for Multiple Families/Groups Structure

A Comprehensive tool for almost all existing multiple testing methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.

Maintained by Yalin Zhu. Last updated 3 years ago.

hierarchical-data multiple-testing multiplicity

25.8 match 2.70 score 9 scripts

bioc

EnhancedVolcano:Publication-ready volcano plots with enhanced colouring and labeling

Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations.

Maintained by Kevin Blighe. Last updated 5 months ago.

rnaseq geneexpression transcription differentialexpression immunooncology

5.9 match 422 stars 11.68 score 2.7k scripts

romanhornung

bapred:Batch Effect Removal and Addon Normalization (in Phenotype Prediction using Gene Data)

Various tools dealing with batch effects, in particular enabling the removal of discrepancies between training and test sets in prediction scenarios. Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008) is implemented to enable integrating the quantile normalization step into prediction rules. The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide an additional function which enables a posteriori ('addon') batch effect removal in independent batches ('test data'). Here, the (already batch effect adjusted) training data is not altered. For evaluating the success of batch effect adjustment several metrics are provided. Moreover, the package implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba() and baaddon() which enable batch effect removal and addon batch effect removal, respectively, with one of the seven methods mentioned above. Another important function here is bametric() which is a wrapper function for all implemented methods for evaluating the success of batch effect removal. For (addon) quantile normalization and (addon) RMA normalization the functions qunormtrain(), qunormaddon(), rmatrain() and rmaaddon() can be used.

Maintained by Roman Hornung. Last updated 3 years ago.

38.6 match 1.78 score 20 scripts

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

3.3 match 400 stars 20.43 score 29k scripts 3.9k dependents

kornl

mutoss:Unified Multiple Testing Procedures

Designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying 'mutossGUI'.

Maintained by Kornelius Rohmeyer. Last updated 12 months ago.

7.7 match 4 stars 8.44 score 24 scripts 16 dependents

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 9 months ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

4.1 match 393 stars 15.68 score 5.0k scripts 13 dependents

openpharma

graphicalMCP:Graphical Multiple Comparison Procedures

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

Maintained by Dong Xi. Last updated 4 months ago.

8.8 match 17 stars 7.35 score 18 scripts

bioc

lumi:BeadArray Specific Methods for Illumina Methylation and Expression Microarrays

The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays.

Maintained by Lei Huang. Last updated 5 months ago.

microarray onechannel preprocessing dnamethylation qualitycontrol twochannel

10.3 match 6.27 score 294 scripts 5 dependents

insightsengineering

rbmi:Reference Based Multiple Imputation

Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Maintained by Isaac Gravestock. Last updated 24 days ago.

7.2 match 18 stars 8.78 score 33 scripts 1 dependents

business-science

anomalize:Tidy Anomaly Detection

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.

Maintained by Matt Dancho. Last updated 1 years ago.

anomaly anomaly-detection decomposition detect-anomalies iqr time-series

6.6 match 339 stars 9.56 score 332 scripts

mayoverse

arsenal:An Arsenal of 'R' Functions for Large-Scale Statistical Summaries

An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models. The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document.

Maintained by Ethan Heinzen. Last updated 7 months ago.

baseline-characteristics descriptive-statistics modeling paired-comparisons reporting statistics tableone

4.7 match 225 stars 13.45 score 1.2k scripts 16 dependents

acclab

dabestr:Data Analysis using Bootstrap-Coupled Estimation

Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

Maintained by Yishan Mai. Last updated 1 years ago.

data-analysis data-visualization estimation statistics

6.3 match 214 stars 9.80 score 142 scripts

mlr-org

mlr3torch:Deep Learning with 'mlr3'

Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'.

Maintained by Sebastian Fischer. Last updated 1 months ago.

data-science deep-learning machine-learning mlr3 torch

8.0 match 42 stars 7.63 score 78 scripts

cran

epiR:Tools for the Analysis of Epidemiological Data

Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.

Maintained by Mark Stevenson. Last updated 2 months ago.

7.4 match 10 stars 8.18 score 10 dependents

bioc

stageR:stageR: stage-wise analysis of high throughput gene expression data in R

The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0

Maintained by Koen Van den Berge. Last updated 5 months ago.

software statisticalmethod

10.6 match 5.72 score 87 scripts

guido-s

metasens:Statistical Methods for Sensitivity Analysis in Meta-Analysis

The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>; - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>; - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.

Maintained by Guido Schwarzer. Last updated 20 hours ago.

adjustment meta-analysis publication-bias rstudio

10.0 match 8 stars 6.01 score 53 scripts

easystats

performance:Assessment of Regression Models Performance

Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.

Maintained by Daniel Lüdecke. Last updated 19 days ago.

aic easystats hacktoberfest loo machine-learning mixed-models models performance r2 statistics

3.7 match 1.1k stars 16.17 score 4.3k scripts 47 dependents

aqlt

ggdemetra:'ggplot2' Extension for Seasonal and Trading Day Adjustment with 'RJDemetra'

Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks.

Maintained by Alain Quartier-la-Tente. Last updated 7 months ago.

ggplot2 jdemetra openjdk

9.7 match 12 stars 6.06 score 16 scripts 1 dependents

spatstat

spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

Maintained by Adrian Baddeley. Last updated 6 hours ago.

cluster-detection confidence-intervals hypothesis-testing k-function roc-curves scan-statistics significance-testing simulation-envelopes spatial-analysis spatial-data-analysis spatial-sharpening spatial-smoothing spatial-statistics

5.8 match 1 stars 10.18 score 67 scripts 149 dependents

allenzhuaz

FixSeqMTP:Fixed Sequence Multiple Testing Procedures

Several generalized / directional Fixed Sequence Multiple Testing Procedures (FSMTPs) are developed for testing a sequence of pre-ordered hypotheses while controlling the FWER, FDR and Directional Error (mdFWER). All three FWER controlling generalized FSMTPs are designed under arbitrary dependence, which allow any number of acceptances. Two FDR controlling generalized FSMTPs are respectively designed under arbitrary dependence and independence, which allow more but a given number of acceptances. Two mdFWER controlling directional FSMTPs are respectively designed under arbitrary dependence and independence, which can also make directional decisions based on the signs of the test statistics. The main functions for each proposed generalized / directional FSMTPs are designed to calculate adjusted p-values and critical values, respectively. For users' convenience, the functions also provide the output option for printing decision rules.

Maintained by Yalin Zhu. Last updated 6 years ago.

multiple-testing pre-order sequential-testing

18.0 match 3 stars 3.22 score 11 scripts

hubbardalex

autostsm:Automatic Structural Time Series Models

Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Maintained by Alex Hubbard. Last updated 9 months ago.

16.3 match 3.55 score 29 scripts

macroeconomicdata

dateutils:Date Utils

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

Maintained by Seth Leonard. Last updated 3 years ago.

data-processing econometrics time-series openblas cpp

10.9 match 3 stars 5.17 score 49 scripts

bioc

trackViewer:A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data

Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data.

Maintained by Jianhong Ou. Last updated 2 months ago.

visualization

6.4 match 8.71 score 145 scripts 2 dependents

branchlab

metasnf:Meta Clustering with Similarity Network Fusion

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

Maintained by Prashanth S Velayudhan. Last updated 5 days ago.

bioinformatics clustering metaclustering snf

6.8 match 8 stars 8.21 score 30 scripts

bioc

sva:Surrogate Variable Analysis

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

Maintained by Jeffrey T. Leek. Last updated 5 months ago.

immunooncology microarray statisticalmethod preprocessing multiplecomparison sequencing rnaseq batcheffect normalization

5.6 match 10.05 score 3.2k scripts 50 dependents

r-spatial

spdep:Spatial Dependence: Weighting Schemes, Statistics

A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and 'Getis/Ord' G ('Ord' and 'Getis' 1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>, 'saddlepoint' approximations ('Tiefelsdorf' 2002) <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') <doi:10.1007/s00168-011-0492-y>. The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022) <doi:10.1111/gean.12319>. 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) <doi:10.1016/0166-0462(95)02111-6>, as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) <doi:10.1080/17421772.2023.2256810>. A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) <doi:10.1016/j.jas.2020.105306> and 'Bivand et al.' (2017) <doi:10.1016/j.spasta.2017.03.003> was added in 1.3-7. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.

Maintained by Roger Bivand. Last updated 19 days ago.

spatial-autocorrelation spatial-dependence spatial-weights

3.4 match 131 stars 16.62 score 6.0k scripts 107 dependents

xsswang

remiod:Reference-Based Multiple Imputation for Ordinal/Binary Response

Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.

Maintained by Tony Wang. Last updated 2 years ago.

bayesian control-based copy-reference delta-adjustment generalized-linear-models glm jags jump-to-reference mcmc missing-at-random missing-data missing-not-at-random multiple-imputation non-ignorable ordinal-regression pattern-mixture-model reference-based statistics cpp

12.9 match 4.30 score 3 scripts

adeverse

ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.

Maintained by Aurélie Siberchicot. Last updated 13 days ago.

openblas cpp

3.7 match 39 stars 14.96 score 2.2k scripts 256 dependents

corentinjgosling

metaConvert:An Automatic Suite for Estimation of Various Effect Size Measures

Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.

Maintained by Corentin J. Gosling. Last updated 4 months ago.

17.3 match 3.18 score 3 scripts

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 3 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

3.5 match 453 stars 15.65 score 1.8k scripts 56 dependents

tim-tu

weibulltools:Statistical Methods for Life Data Analysis

Provides statistical methods and visualizations that are often used in reliability engineering. Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, <ISBN:978-0-9653062-3-2>), Johnson (Johnson, 1964, <ISBN:978-0444403223>), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, <DOI:10.1080/08982112.2010.503447>) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, <ISBN:9780471673279>) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported.

Maintained by Tim-Gunnar Hensel. Last updated 2 years ago.

field-data-analysis interactive-visualizations plotly reliability-analysis weibull-analysis weibulltools openblas cpp

8.9 match 13 stars 6.15 score 54 scripts

eheinzen

elo:Ranking Teams by Elo Rating and Comparable Methods

A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models.

Maintained by Ethan Heinzen. Last updated 1 years ago.

elo elo-rating logistic-regression markov-chain markov-model ranking sports-analytics cpp

7.7 match 37 stars 7.05 score 153 scripts

pcbrendel

multibias:Simultaneous Multi-Bias Adjustment

Quantify the causal effect of a binary exposure on a binary outcome with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the concept of combining inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Maintained by Paul Brendel. Last updated 21 days ago.

causal-inference causal-models epidemiology

9.9 match 5.34 score 7 scripts

animint

animint2:Animated Interactive Grammar of Graphics

Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, <doi:10.1080/10618600.2018.1513367> describes the concepts implemented.

Maintained by Toby Hocking. Last updated 28 days ago.

5.8 match 64 stars 8.87 score 173 scripts

victormesquita40

DFA:Detrended Fluctuation Analysis

Contains the Detrended Fluctuation Analysis (DFA), Detrended Cross-Correlation Analysis (DCCA), Detrended Cross-Correlation Coefficient (rhoDCCA), Delta Amplitude Detrended Cross-Correlation Coefficient (DeltarhoDCCA), log amplitude Detrended Fluctuation Analysis (DeltalogDFA), two DFA automatic methods for identification of crossover points and a Deltalog automatic method for identification of reference channels.

Maintained by Victor Barreto Mesquita. Last updated 1 years ago.

12.7 match 4.04 score 22 scripts

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

11.3 match 7 stars 4.54 score 8 scripts

bioc

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq sequencing geneexpression spatial singlecell transcriptomics preprocessing rna-seq spatial-transcriptomics

7.9 match 28 stars 6.48 score 36 scripts

daattali

timevis:Create Interactive Timeline Visualizations in R

Create rich and fully interactive timeline visualizations. Timelines can be included in Shiny apps or R markdown documents. 'timevis' includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Based on the 'vis.js' Timeline JavaScript library.

Maintained by Dean Attali. Last updated 7 months ago.

shiny shiny-r

5.4 match 671 stars 9.41 score 410 scripts 5 dependents

pln-team

PLNmodels:Poisson Lognormal Models

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Maintained by Julien Chiquet. Last updated 5 days ago.

count-data multivariate-analysis network-inference pca poisson-lognormal-model openblas cpp

5.3 match 56 stars 9.50 score 226 scripts

dbasu-umass

bate:Computes Bias-Adjusted Treatment Effect

Compute bounds for the treatment effect after adjusting for the presence of omitted variables in linear econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. You supply the data, identify the outcome and treatment variables and additional regressors. The main functions will compute bounds for the bias-adjusted treatment effect. Many plot functions allow easy visualization of results.

Maintained by Deepankar Basu. Last updated 2 years ago.

13.7 match 3.70 score 2 scripts

guido-s

netmeta:Network Meta-Analysis using Frequentist Methods

A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - subgroup network meta-analysis.

Maintained by Guido Schwarzer. Last updated 3 days ago.

meta-analysis network-meta-analysis rstudio

4.3 match 33 stars 11.82 score 199 scripts 10 dependents

epiforecasts

EpiNow2:Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.

Maintained by Sebastian Funk. Last updated 26 days ago.

backcalculation covid-19 gaussian-processes open-source reproduction-number stan cpp

4.2 match 120 stars 11.88 score 210 scripts

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.

10.1 match 4.90 score 366 scripts 4 dependents

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 27 days ago.

cpp

5.3 match 11 stars 9.19 score 226 scripts 2 dependents

usepa

spsurvey:Spatial Sampling Design and Analysis

A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.

Maintained by Michael Dumelle. Last updated 7 months ago.

ord

5.4 match 15 stars 8.98 score 241 scripts 1 dependents

youjin1207

logisticRR:Adjusted Relative Risk from Logistic Regression

Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.

Maintained by Youjin Lee. Last updated 5 years ago.

logistic-regression odds-ratio relative-risk

14.2 match 3 stars 3.38 score 16 scripts

mpiktas

midasr:Mixed Data Sampling Regression

Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

Maintained by Vaidotas Zemlys-Balevičius. Last updated 3 years ago.

8.3 match 77 stars 5.76 score 150 scripts

hojsgaard

pbkrtest:Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

Computes p-values based on (a) Satterthwaite or Kenward-Rogers degree of freedom methods and (b) parametric bootstrap for mixed effects models as implemented in the 'lme4' package. Implements parametric bootstrap test for generalized linear mixed models as implemented in 'lme4' and generalized linear models. The package is documented in the paper by Halekoh and Højsgaard, (2012, <doi:10.18637/jss.v059.i09>). Please see 'citation("pbkrtest")' for citation details.

Maintained by Søren Højsgaard. Last updated 11 days ago.

3.3 match 5 stars 14.36 score 648 scripts 915 dependents

mjskay

ggblend:Blending and Compositing Algebra for 'ggplot2'

Algebra of operations for blending, copying, adjusting, and compositing layers in 'ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.

Maintained by Matthew Kay. Last updated 2 years ago.

7.5 match 184 stars 6.29 score 71 scripts 1 dependents

detlew

PowerTOST:Power and Sample Size for (Bio)Equivalence Studies

Contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information.

Maintained by Detlew Labes. Last updated 12 months ago.

4.9 match 20 stars 9.61 score 112 scripts 4 dependents

pik-piam

edgeTransport:Prepare EDGE Transport Data for the REMIND model

EDGE-T is a fork of the GCAM transport module https://jgcri.github.io/gcam-doc/energy.html#transportation with a high level of detail in its representation of technological and modal options. It is a partial equilibrium model with a nested multinomial logit structure and relies on the modified logit formulation. Most of the sources are not publicly available. PIK-internal users can find the sources in the distributed file system in the folder `/p/projects/rd3mod/inputdata/sources/EDGE-Transport-Standalone`.

Maintained by Johanna Hoppe. Last updated 3 days ago.

6.9 match 5 stars 6.84 score 16 scripts 2 dependents

ziyili20

caROC:Continuous Biomarker Evaluation with Adjustment of Covariates

Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment".

Maintained by Ziyi Li. Last updated 4 years ago.

23.5 match 2.00 score 5 scripts

bioc

methylKit:DNA methylation analysis from high-throughput bisulfite sequencing results

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files.

Maintained by Altuna Akalin. Last updated 17 days ago.

dnamethylation sequencing methylseq genome-biology methylation statistical-analysis visualization curl bzip2 xz-utils zlib cpp

4.0 match 220 stars 11.80 score 578 scripts 3 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

4.1 match 11.12 score 2.5k scripts 98 dependents

matthieustigler

partsm:Periodic Autoregressive Time Series Models

Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.

Maintained by Matthieu Stigler. Last updated 4 years ago.

10.0 match 3 stars 4.57 score 25 scripts

bommert

stabm:Stability Measures for Feature Selection

An implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available, see Bommert (2020) <doi:10.17877/DE290R-21906>.

Maintained by Andrea Bommert. Last updated 2 years ago.

7.2 match 6 stars 6.25 score 33 scripts 3 dependents

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 6 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

3.4 match 13.40 score 17k scripts 255 dependents

flr

FLCore:Core Package of FLR, Fisheries Modelling in R

Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.

Maintained by Iago Mosqueira. Last updated 10 days ago.

fisheries flr fisheries-modelling

5.1 match 16 stars 8.78 score 956 scripts 23 dependents

openpharma

mmrm:Mixed Models for Repeated Measures

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.

Maintained by Daniel Sabanes Bove. Last updated 10 days ago.

cpp

3.6 match 138 stars 12.15 score 113 scripts 4 dependents

argocanada

argoFloats:Analysis of Oceanographic Argo Floats

Supports the analysis of oceanographic data recorded by Argo autonomous drifting profiling floats. Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) <doi:10.3389/fmars.2021.635922> for more on the scientific context and applications.

Maintained by Dan Kelley. Last updated 1 months ago.

6.0 match 17 stars 7.32 score 203 scripts

bioc

scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

Maintained by Hechen Li. Last updated 5 months ago.

singlecell transcriptomics geneexpression sequencing experimentaldesign

6.1 match 23 stars 7.15 score 11 scripts

alexkowa

EnvStats:Package for Environmental Statistics, Including US EPA Guidance

Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, <doi:10.1007/978-1-4614-8456-1>).

Maintained by Alexander Kowarik. Last updated 17 days ago.

3.3 match 26 stars 12.80 score 2.4k scripts 46 dependents

bfifield

hettx:Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>, and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <arXiv:1605.06566> for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

Maintained by Ben Fifield. Last updated 2 years ago.

8.0 match 10 stars 5.32 score 21 scripts

cran

ADLP:Accident and Development Period Adjusted Linear Pools for Actuarial Stochastic Reserving

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

Maintained by Yanfeng Li. Last updated 11 months ago.

15.8 match 2.70 score

aqlt

rjdqa:Quality Assessment for Seasonal Adjustment

Add-in to the 'RJDemetra' package on seasonal adjustments. It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.

Maintained by Alain Quartier-la-Tente. Last updated 4 months ago.

jdemetra quality-assessment openjdk

11.0 match 2 stars 3.85 score 8 scripts

epiverse-trace

cfr:Estimate Disease Severity and Case Ascertainment

Estimate the severity of a disease and ascertainment of cases, as discussed in Nishiura et al. (2009) <doi:10.1371/journal.pone.0006852>.

Maintained by Adam Kucharski. Last updated 17 days ago.

case-fatality-rate epidemic-modelling epidemiology epiverse health-outcomes outbreak-analysis sdg-3

5.2 match 13 stars 8.15 score 35 scripts

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

2.3 match 1.1k stars 18.63 score 16k scripts 239 dependents

bioc

missMethyl:Analysing Illumina HumanMethylation BeadChip Data

Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.

Maintained by Belinda Phipson. Last updated 14 days ago.

normalization dnamethylation methylationarray genomicvariation geneticvariability differentialmethylation genesetenrichment

5.8 match 7.24 score 300 scripts 1 dependents

stopsack

batchtma:Batch Effect Adjustments

Different adjustment methods for batch effects in biomarker data, such as from tissue microarrays. Some methods attempt to retain differences between batches that may be due to between-batch differences in "biological" factors that influence biomarker values.

Maintained by Konrad Stopsack. Last updated 9 months ago.

batch-effects measurement-error tissue-microarray-analysis

11.2 match 1 stars 3.70 score 3 scripts

bioc

cytomapper:Visualization of highly multiplexed imaging data in R

Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.

Maintained by Lasse Meyer. Last updated 5 months ago.

immunooncology software singlecell onechannel twochannel multiplecomparison normalization dataimport bioimaging imaging-mass-cytometry single-cell spatial-analysis

4.3 match 32 stars 9.61 score 354 scripts 5 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

4.0 match 23 stars 10.01 score 280 scripts 22 dependents

r-forge

multcomp:Simultaneous Inference in General Parametric Models

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

Maintained by Torsten Hothorn. Last updated 2 months ago.

3.0 match 13.49 score 7.5k scripts 366 dependents

sssydysss

TransProR:Analysis and Visualization of Multi-Omics Data

A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

Maintained by Dongyue Yu. Last updated 20 days ago.

5.3 match 174 stars 7.55 score 34 scripts

nepem-ufsc

metan:Multi Environment Trials Analysis

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

Maintained by Tiago Olivoto. Last updated 10 days ago.

4.3 match 2 stars 9.48 score 1.3k scripts 2 dependents

aqlt

ggdemetra3:'ggplot2' Extension for Seasonal and Trading Day Adjustment with 'JDemetra+' 3.0

Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment made by the R interface to 'JDemetra+' 3.0.

Maintained by Alain Quartier-la-Tente. Last updated 3 months ago.

openjdk

9.3 match 4 stars 4.26 score 8 scripts 1 dependents

grosssbm

missSBM:Handling Missing Data in Stochastic Block Models

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>, adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

Maintained by Julien Chiquet. Last updated 4 days ago.

missing-data nas network-analysis network-dataset stochastic-block-model cpp

7.1 match 12 stars 5.53 score 19 scripts

x13org

x13binary:Provide the 'x13ashtml' Seasonal Adjustment Binary

The US Census Bureau provides a seasonal adjustment program now called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.

Maintained by Dirk Eddelbuettel. Last updated 8 months ago.

fortran

5.2 match 10 stars 7.50 score 16 scripts 10 dependents

wincowgerdev

OpenSpecy:Analyze, Process, Identify, and Share Raman and (FT)IR Spectra

Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, <doi:10.1021/acs.analchem.1c00123>). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library (Cowger et al. 2020, <doi:10.1177/0003702820929064>) using match_spec(). A Shiny app is available via run_app() or online at <https://openanalysis.org/openspecy/>.

Maintained by Win Cowger. Last updated 17 days ago.

5.1 match 29 stars 7.58 score 22 scripts

vegandevs

vegan:Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Maintained by Jari Oksanen. Last updated 17 days ago.

ecological-modelling ecology ordination fortran openblas

2.0 match 472 stars 19.41 score 15k scripts 440 dependents

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

3.5 match 10.93 score 10k scripts 55 dependents

bioc

LPE:Methods for analyzing microarray data using Local Pooled Error (LPE) method

This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library.

Maintained by Nitin Jain. Last updated 5 months ago.

microarray differentialexpression

8.3 match 4.58 score 21 scripts 1 dependents

monty-se

PINstimation:Estimation of the Probability of Informed Trading

A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

Maintained by Montasser Ghachem. Last updated 5 months ago.

clustering-analysis expectation-maximisation-algorithm hierarchical-clustering information-asymmetry market-microstructure maximum-likelihood-estimation mixture-distributions poisson-distribution

5.8 match 36 stars 6.48 score 14 scripts

fgazzelloni

hmsidwR:Health Metrics and the Spread of Infectious Diseases

A collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.

Maintained by Federica Gazzelloni. Last updated 2 months ago.

deaths health-data infectious-diseases lifeexpectancy

6.9 match 4 stars 5.48 score 6 scripts

bioc

DAPAR:Tools for the Differential Analysis of Proteins Abundance with R

The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package).

Maintained by Samuel Wieczorek. Last updated 5 months ago.

proteomics normalization preprocessing massspectrometry qualitycontrol go dataimport prostar1

6.9 match 2 stars 5.42 score 22 scripts 1 dependents

cran

FuzzySTs:Fuzzy Statistical Tools

The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the book entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" <doi: 10.1007/978-3-030-76916-1>. For the theoretical approaches, see Berkachy R. and Donze L. (2019) <doi:10.1007/978-3-030-03368-2_1>. For the empirical approaches, see Berkachy R. and Donze L. (2016) <ISBN: 978-989-758-201-1>). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance with all its properties. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen.

Maintained by Redina Berkachy. Last updated 8 months ago.

10.9 match 3.40 score

bioc

dittoSeq:User Friendly Single-Cell and Bulk RNA Sequencing Visualization

A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors().

Maintained by Daniel Bunis. Last updated 5 months ago.

software visualization rnaseq singlecell geneexpression transcriptomics dataimport

4.8 match 7.56 score 760 scripts 2 dependents

qlcal

qlcal:R Bindings to the Calendaring Functionality of 'QuantLib'

'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now maintained separately just for the calendaring subset). See the included file 'AUTHORS' for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').

Maintained by Dirk Eddelbuettel. Last updated 30 days ago.

cpp

7.3 match 7 stars 4.99 score 28 scripts

beckerbenj

eatGADS:Data Management of Large Hierarchical Data

Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.

Maintained by Benjamin Becker. Last updated 24 days ago.

4.9 match 1 stars 7.36 score 34 scripts 1 dependents

bristol-vaccine-centre

testerror:Uncertainty in Multiplex Panel Testing

Provides methods to support the estimation of epidemiological parameters based on the results of multiplex panel tests.

Maintained by Robert Challen. Last updated 12 months ago.

10.6 match 1 stars 3.40 score 4 scripts

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

3.7 match 45 stars 9.70 score 268 scripts 16 dependents

gavinsimpson

gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.

Maintained by Gavin L. Simpson. Last updated 21 hours ago.

distributional-regression gam gamm generalized-additive-mixed-models generalized-additive-models ggplot2 glm lm mgcv penalized-spline random-effects smoothing splines

2.8 match 217 stars 12.99 score 1.6k scripts 2 dependents

kassambara

survminer:Drawing Survival Curves using 'ggplot2'

Contains the function 'ggsurvplot()' for drawing easily beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. Other functions are also available to plot adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.

Maintained by Alboukadel Kassambara. Last updated 5 months ago.

2.2 match 524 stars 15.87 score 7.0k scripts 55 dependents

resplab

predtools:Prediction Model Tools

Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Maintained by Amin Adibi. Last updated 2 years ago.

cpp

5.3 match 9 stars 6.74 score 77 scripts

ocbe-uio

contingencytables:Statistical Analysis of Contingency Tables

Provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.

Maintained by Waldir Leoncio. Last updated 7 months ago.

contingency-table

8.5 match 3 stars 4.13 score 8 scripts 1 dependents

cnuge

debar:A Post-Clustering Denoiser for COI-5P Barcode Data

The 'debar' sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package is designed to detect and correct insertion and deletion errors within sequencer outputs. This is accomplished through comparison of input sequences against a profile hidden Markov model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted for through the introduction of a placeholder character. Since the PHMM is a probabilistic representation of the COI barcode, corrections are not always perfect. For this reason 'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder characters (default is 7 base pairs in either direction, this feature can be disabled). Testing has shown that this censorship results in the correct sequence length being restored, and erroneous base pairs being masked the vast majority of the time (>95%).

Maintained by Cameron M. Nugent. Last updated 1 years ago.

bioinformatics denoising dna-barcoding dna-sequencing hidden-markov-model machine-learning

8.8 match 1 stars 4.00 score 8 scripts

merck

psm3mkv:Evaluate Partitioned Survival and State Transition Models

Fits and evaluates three-state partitioned survival analyses (PartSAs) and Markov models (clock forward or clock reset) to progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.

Maintained by Dominic Muston. Last updated 9 months ago.

5.5 match 10 stars 6.43 score 1 scripts

mages

ChainLadder:Statistical Methods and Models for Claims Reserving in General Insurance

Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.

Maintained by Markus Gesmann. Last updated 1 months ago.

3.5 match 82 stars 10.04 score 196 scripts 2 dependents

peterbiber

viscomplexr:Phase Portraits of Functions in the Complex Number Plane

Functionality for creating phase portraits of functions in the complex number plane. Works with R base graphics, whose full functionality is available. Parallel processing is used for optimum performance.

Maintained by Peter Biber. Last updated 4 months ago.

cpp

7.3 match 4 stars 4.75 score 14 scripts

cwolock

survML:Tools for Flexible Survival Analysis Using Machine Learning

Statistical tools for analyzing time-to-event data using machine learning. Implements survival stacking for conditional survival estimation, standardized survival function estimation for current status data, and methods for algorithm-agnostic variable importance. See Wolock CJ, Gilbert PB, Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.

Maintained by Charles Wolock. Last updated 2 months ago.

4.3 match 16 stars 8.06 score 73 scripts 1 dependents

rstudio

gt:Easily Create Presentation-Ready Display Tables

Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which 'gt' handles things for you means that you don't often have to worry about the fine details.

Maintained by Richard Iannone. Last updated 12 days ago.

docx easy-to-use html latex rtf summary-tables

1.9 match 2.1k stars 18.36 score 20k scripts 112 dependents

poissonconsulting

dttr2:Manipulate Date, POSIXct and hms Vectors

Manipulates date ('Date'), date time ('POSIXct') and time ('hms') vectors. Date/times are considered discrete and are floored whenever encountered. Times are wrapped and time zones are maintained unless explicitly altered by the user.

Maintained by Ayla Pearson. Last updated 2 months ago.

6.0 match 10 stars 5.65 score 5 scripts 6 dependents

cran

VAM:Variance-Adjusted Mahalanobis

Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.

Maintained by H. Robert Frost. Last updated 1 years ago.

7.1 match 4.78 score 4 dependents

bluefoxr

COINr:Composite Indicator Construction and Analysis

A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.

Maintained by William Becker. Last updated 2 months ago.

3.8 match 26 stars 9.07 score 73 scripts 1 dependents

eddelbuettel

RcppQuantuccia:R Bindings to the Calendaring Functionality of 'QuantLib'

'QuantLib' bindings are provided for R using 'Rcpp' via an updated variant of the header-only 'Quantuccia' project (put together initially by Peter Caspers) offering an essential subset of 'QuantLib' (and now maintained separately for the calendaring subset). See the included file 'AUTHORS' for a full list of contributors to both 'QuantLib' and 'Quantuccia'.

Maintained by Dirk Eddelbuettel. Last updated 4 months ago.

cpp11 quantlib cpp

7.3 match 12 stars 4.62 score 23 scripts

leifeld

btergm:Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Maintained by Philip Leifeld. Last updated 12 months ago.

complex-networks dynamic-analysis ergm estimation goodness-of-fit inference longitudinal-data network-analysis prediction tergm

5.0 match 17 stars 6.70 score 83 scripts 2 dependents

carloscinelli

sensemakr:Sensitivity Analysis Tools for Regression Models

Implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

Maintained by Carlos Cinelli. Last updated 4 months ago.

3.5 match 92 stars 9.43 score 139 scripts 2 dependents

dhaine

episensr:Basic Sensitivity Analysis of Epidemiological Results

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2021).

Maintained by Denis Haine. Last updated 1 years ago.

bias epidemiology sensitivity-analysis statistics

5.1 match 13 stars 6.48 score 39 scripts 1 dependents

bioc

ggtreeExtra:An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree"

'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'.

Maintained by Shuangbin Xu. Last updated 5 months ago.

software visualization phylogenetics annotation

3.4 match 90 stars 9.72 score 426 scripts 3 dependents

openpharma

DoseFinding:Planning and Analyzing Dose Finding Experiments

The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).

Maintained by Marius Thomas. Last updated 5 days ago.

openblas

3.2 match 8 stars 10.32 score 98 scripts 10 dependents

matloff

qeML:Quick and Easy Machine Learning Tools

The letters 'qe' in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface.

Maintained by Norm Matloff. Last updated 26 days ago.

3.9 match 41 stars 8.41 score 48 scripts 1 dependents

marcelschweiker

comf:Models and Equations for Human Comfort Research

Calculation of various common and less common comfort indices such as predicted mean vote or the two node model. Converts physical variables such as relative to absolute humidity and evaluates the performance of comfort indices.

Maintained by Marcel Schweiker. Last updated 3 months ago.

6.8 match 3 stars 4.78 score 40 scripts

barcaroli

SamplingStrata:Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys

In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package.

Maintained by Giulio Barcaroli. Last updated 15 days ago.

cpp

4.3 match 15 stars 7.60 score 178 scripts

davidgohel

flextable:Functions for Tabular Reporting

Use a grammar for creating and customizing pretty tables. The following formats are supported: 'HTML', 'PDF', 'RTF', 'Microsoft Word', 'Microsoft PowerPoint' and R 'Grid Graphics'. 'R Markdown', 'Quarto' and the package 'officer' can be used to produce the result files. The syntax is the same for the user regardless of the type of output to be produced. A set of functions allows the creation, definition of cell arrangement, addition of headers or footers, formatting and definition of cell content with text and or images. The package also offers a set of high-level functions that allow tabular reporting of statistical models and the creation of complex cross tabulations.

Maintained by David Gohel. Last updated 1 months ago.

docx html5 ms-office-documents rmarkdown table

1.9 match 583 stars 17.04 score 7.3k scripts 119 dependents

rjdverse

rjd3x13:Seasonal Adjustment with X-13 in 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers full acces to options and outputs of X-13, including RegARIMA modelling (automatic ARIMA model with outlier detection and trading days adjustment) and X-11 decomposition.

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

openjdk

7.0 match 5 stars 4.53 score 8 scripts 3 dependents

gforge

Greg:Regression Helper Functions

Methods for manipulating regression models and for describing these in a style adapted for medical journals. Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich' package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for time-splitting a dataset when modeling non-proportional hazards in Cox regressions.

Maintained by Max Gordon. Last updated 1 years ago.

5.1 match 6 stars 6.26 score 68 scripts

ddsjoberg

gtsummary:Presentation-Ready Data Summary and Analytic Result Tables

Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Maintained by Daniel D. Sjoberg. Last updated 3 days ago.

easy-to-use gt html5 regression-models reproducibility reproducible-research statistics summary-statistics summary-tables table1 tableone

1.9 match 1.1k stars 17.00 score 8.2k scripts 15 dependents

trackage

trip:Tracking Data

Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.

Maintained by Michael D. Sumner. Last updated 8 months ago.

4.1 match 13 stars 7.72 score 137 scripts 1 dependents

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.

5.5 match 31 stars 5.75 score 12 scripts

rjdverse

rjd3tramoseats:Seasonal Adjustment with TRAMO-SEATS in 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers full acces to options and outputs of TRAMO-SEATS (Time series Regression with ARIMA noise, Missing values and Outliers - Signal Extraction in ARIMA Time Series), including TRAMO modelling (automatic ARIMA model with outlier detection and trading days adjustment).

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

openjdk

7.0 match 5 stars 4.51 score 12 scripts 3 dependents

bioc

tidybulk:Brings transcriptomics to the tidyverse

This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics bioconductor bulk-transcriptional-analyses deseq2 differential-expression edger ensembl-ids entrez gene-symbols gsea mds-dimensions pca pipe redundancy tibble tidy tidy-data tidyverse transcripts tsne

3.3 match 168 stars 9.48 score 172 scripts 1 dependents

jokergoo

circlize:Circular Visualization

Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.

Maintained by Zuguang Gu. Last updated 1 years ago.

2.0 match 983 stars 15.62 score 10k scripts 213 dependents

numbats

cassowaryr:Compute Scagnostics on Pairs of Numeric Variables in a Data Set

Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) <doi:10.1198/106186008X320465> and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided.

Maintained by Harriet Mason. Last updated 12 days ago.

data-science data-visualization eda high-dimensional-data multivariate

5.2 match 3 stars 6.02 score 26 scripts 1 dependents

camhowitt

shinyLottie:Seamlessly Integrate 'Lottie' Animations into 'shiny' Applications

Easily integrate and control 'Lottie' animations within 'shiny' applications', without the need for idiosyncratic expression or use of 'JavaScript'. This includes utilities for generating animation instances, controlling playback, manipulating animation properties, and more. For more information on 'Lottie', see: <https://airbnb.io/lottie/#/>. Additionally, see the official 'Lottie' GitHub repository at <https://github.com/airbnb/lottie>.

Maintained by Cameron Howitt. Last updated 9 months ago.

lottie shiny

5.6 match 2 stars 5.52 score 37 scripts

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

3.4 match 7 stars 9.11 score 1.3k scripts 6 dependents

feiyoung

ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

Maintained by Wei Liu. Last updated 1 months ago.

openblas cpp

5.3 match 2 stars 5.86 score 12 scripts 1 dependents

andymckenzie

BRETIGEA:Brain Cell Type Specific Gene Expression Analysis

Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.

Maintained by Andrew McKenzie. Last updated 1 years ago.

cell-type gene-expression gene-expression-signatures

4.9 match 15 stars 6.20 score 30 scripts

r-forge

colorspace:A Toolbox for Manipulating and Assessing Colors and Palettes

Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, <doi:10.18637/jss.v096.i01>).

Maintained by Achim Zeileis. Last updated 4 months ago.

1.9 match 16.23 score 8.2k scripts 8.1k dependents

bioc

cola:A Framework for Consensus Partitioning

Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner.

Maintained by Zuguang Gu. Last updated 1 months ago.

clustering geneexpression classification software consensus-clustering cpp

4.0 match 61 stars 7.49 score 112 scripts

pecanproject

PEcAn.dvmdostem:PEcAn Package for Integration of the Dvmdostem Model

This module provides functions to link the dvmdostem model to PEcAn.

Maintained by Tobey Carman. Last updated 3 days ago.

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

4.0 match 216 stars 7.56 score 3 scripts

bayesball

ProbBayes:Probability and Bayesian Modeling

Functions and datasets to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).

Maintained by Jim Albert. Last updated 4 years ago.

7.0 match 5 stars 4.30 score 80 scripts

agi-lab

SynthETIC:Synthetic Experience Tracking Insurance Claims

Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <arXiv:2008.05693>.

Maintained by Melantha Wang. Last updated 1 years ago.

4.8 match 12 stars 6.22 score 23 scripts 2 dependents

nutterb

pixiedust:Tables so Beautifully Fine-Tuned You Will Believe It's Magic

The introduction of the 'broom' package has made converting model objects into data frames as simple as a single function. While the 'broom' package focuses on providing tidy data frames that can be used in advanced analysis, it deliberately stops short of providing functionality for reporting models in publication-ready tables. 'pixiedust' provides this functionality with a programming interface intended to be similar to 'ggplot2's system of layers with fine tuned control over each cell of the table. Options for output include printing to the console and to the common markdown formats (markdown, HTML, and LaTeX). With a little 'pixiedust' (and happy thoughts) tables can really fly.

Maintained by Benjamin Nutter. Last updated 1 years ago.

3.8 match 180 stars 8.01 score 94 scripts

kassambara

ggpubr:'ggplot2' Based Publication Ready Plots

The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.

Maintained by Alboukadel Kassambara. Last updated 2 years ago.

1.8 match 1.2k stars 16.68 score 65k scripts 409 dependents

bioc

Repitools:Epigenomic tools

Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc.

Maintained by Mark Robinson. Last updated 5 months ago.

dnamethylation geneexpression methylseq

5.1 match 5.90 score 267 scripts

devopifex

g2r:Interactive Grammar of Graphics

Interactive grammar of graphics.

Maintained by John Coene. Last updated 3 years ago.

visualization

6.6 match 119 stars 4.53 score 57 scripts

olink-proteomics

OlinkAnalyze:Facilitate Analysis of Proteomic Data from Olink

A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

Maintained by Kathleen Nevola. Last updated 21 days ago.

olink proteomics proteomics-data-analysis

3.1 match 104 stars 9.72 score 61 scripts

mlr-org

mlr3filters:Filter Based Feature Selection for 'mlr3'

Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.

Maintained by Marc Becker. Last updated 4 months ago.

feature-selection filter filters mlr mlr3 variable-importance

3.5 match 20 stars 8.37 score 95 scripts 3 dependents

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 17 hours ago.

fortran

1.7 match 210 stars 17.61 score 17k scripts 750 dependents

jomulder

BFpack:Flexible Bayes Factor Testing of Scientific Expectations

Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. The statistical underpinnings are described in O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>, De Santis and Spezzaferri (2001) <DOI:10.1016/S0378-3758(00)00240-8>, Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>, Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>, Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>, Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>, Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>, Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18> and the relevant methodological papers.

Maintained by Joris Mulder. Last updated 1 months ago.

fortran openblas

3.6 match 15 stars 8.24 score 55 scripts 3 dependents

jeksterslab

betaSandwich:Robust Confidence Intervals for Standardized Regression Coefficients

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

Maintained by Ivan Jacob Agaloos Pesigan. Last updated 2 months ago.

confidence-intervals heteroskedasticity-consistent-standard-errors standardized-regression-coefficients

7.1 match 4.16 score 16 scripts

hneth

unikn:Graphical Elements of the University of Konstanz's Corporate Design

Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other purposes or institutions.

Maintained by Hansjoerg Neth. Last updated 3 months ago.

branding color color-palette colorscheme corporate-design palette text-decoration university-colors visual-identity

3.3 match 39 stars 8.82 score 156 scripts 2 dependents

lau-mel

swamp:Visualization, Analysis and Adjustment of High-Dimensional Data in Respect to Sample Annotations

Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.

Maintained by Martin Lauss. Last updated 5 years ago.

12.1 match 2.42 score 29 scripts 1 dependents

ohdsi

EmpiricalCalibration:Routines for Performing Empirical Calibration of Observational Study Estimates

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

Maintained by Martijn Schuemie. Last updated 1 months ago.

hades cpp

3.4 match 10 stars 8.51 score 151 scripts 1 dependents

cran

svgtools:Manipulate SVG (Template) Files of Charts

The purpose of this package is to manipulate SVG files that are templates of charts the user wants to produce. In vector graphics one copes with x-/y-coordinates of elements (e.g. lines, rectangles, text). Their scale is often dependent on the program that is used to produce the graphics. In applied statistics one usually has numeric values on a fixed scale (e.g. percentage values between 0 and 100) to show in a chart. Basically, 'svgtools' transforms the statistical values into coordinates and widths/heights of the vector graphics. This is done by stackedBar() for bar charts, by linesSymbols() for charts with lines and/or symbols (dot markers) and scatterSymbols() for scatterplots.

Maintained by Christian Wimmer. Last updated 9 months ago.

14.5 match 2.00 score

agqhammond

UKFE:UK Flood Estimation

Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 13. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Proposal of the extreme rank plot for extreme value analysis: with an emphasis on flood frequency studies", Hammond (2019, <doi:10.2166/nh.2019.157>). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, <doi:10.2166/nh.2021.059>). "Improving the FEH statistical procedures for flood frequency estimation", Environment Agency (2008, ISBN: 978 1 84432 920 5). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Wallingford HydroSolutions, (2016, <http://software.hydrosolutions.co.uk/winfap4/Urban-Adjustment-Procedure-Technical-Note.pdf>). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>).

Maintained by Anthony Hammond. Last updated 1 months ago.

16.2 match 1 stars 1.78 score