Showing 200 of total 2596 results (show query)
r-quantities
errors:Uncertainty Propagation for R Vectors
Support for measurement errors in R vectors, matrices and arrays: automatic uncertainty propagation and reporting. Documentation about 'errors' is provided in the paper by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a vignette; see 'citation("errors")' for details.
Maintained by Iñaki Ucar. Last updated 2 months ago.
81.9 match 49 stars 8.18 score 86 scripts 4 dependentsmlr-org
mlr3:Machine Learning in R - Next Generation
Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.
Maintained by Marc Becker. Last updated 5 days ago.
classificationdata-sciencemachine-learningmlr3regression
33.4 match 972 stars 14.86 score 2.3k scripts 35 dependentsmfrasco
Metrics:Evaluation Metrics for Machine Learning
An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.
Maintained by Michael Frasco. Last updated 6 years ago.
35.7 match 99 stars 13.02 score 6.1k scripts 51 dependentsr-lib
rlang:Functions for Base Types and Core R and 'Tidyverse' Features
A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.
Maintained by Lionel Henry. Last updated 20 days ago.
18.9 match 517 stars 20.53 score 9.8k scripts 15k dependentssckott
fauxpas:HTTP Error Helpers
HTTP error helpers. Methods included for general purpose HTTP error handling, as well as individual methods for every HTTP status code, both via status code numbers as well as their descriptive names. Supports ability to adjust behavior to stop, message or warning. Includes ability to use custom whisker template to have any configuration of status code, short description, and verbose message. Currently supports integration with 'crul', 'curl', and 'httr'.
Maintained by Scott Chamberlain. Last updated 1 years ago.
httphttpsapiweb-servicescurlerrorserrorerror-handling
44.0 match 15 stars 8.07 score 36 scripts 24 dependentsrstudio
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 4 days ago.
26.0 match 845 stars 13.57 score 264 scripts 2 dependentsbioc
dada2:Accurate, high-resolution sample inference from amplicon sequencing data
The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.
Maintained by Benjamin Callahan. Last updated 5 months ago.
immunooncologymicrobiomesequencingclassificationmetagenomicsampliconbioconductorbioinformaticsmetabarcodingtaxonomycpp
25.7 match 485 stars 13.17 score 3.0k scripts 4 dependentspsychmeta
psychmeta:Psychometric Meta-Analysis Toolkit
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
Maintained by Jeffrey A. Dahlke. Last updated 9 months ago.
hacktoberfestmeta-analysispsychologypsychometricpsychometrics
39.3 match 57 stars 8.25 score 151 scriptsr-lib
testthat:Unit Testing for R
Software testing is important, but, in part because it is frustrating and boring, many of us avoid it. 'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'.
Maintained by Hadley Wickham. Last updated 16 days ago.
12.5 match 900 stars 20.97 score 74k scripts 465 dependentsfranzmohr
bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Maintained by Franz X. Mohr. Last updated 1 years ago.
bayesianbayesian-inferencebayesian-varbvarbvecmgibbs-samplingmcmcvector-autoregressionvector-error-correction-modelopenblascpp
37.9 match 31 stars 6.80 score 34 scripts 1 dependentsropensci
assertr:Assertive Programming for R Analysis Pipelines
Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to 'stopifnot()' but more powerful, friendly, and easier for use in pipelines.
Maintained by Tony Fischetti. Last updated 11 months ago.
analysis-pipelineassertion-libraryassertion-methodsassertionspeer-reviewedpredicate-functions
22.3 match 478 stars 11.39 score 452 scripts 12 dependentsverasls
lvmisc:Veras Miscellaneous
Contains a collection of useful functions for basic data computation and manipulation, wrapper functions for generating 'ggplot2' graphics, including statistical model diagnostic plots, methods for computing statistical models quality measures (such as AIC, BIC, r squared, root mean squared error) and general utilities.
Maintained by Lucas Veras. Last updated 1 years ago.
45.8 match 6 stars 5.40 score 14 scripts 1 dependentslrberge
fixest:Fast Fixed-Effects Estimations
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Maintained by Laurent Berge. Last updated 7 months ago.
15.6 match 387 stars 14.69 score 3.8k scripts 25 dependentsdcousin3
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-barsplottingstatisticssummary-plotssummary-statisticsvisualization
23.4 match 19 stars 9.55 score 155 scripts 2 dependentsphilchalmers
SimDesign:Structure for Organizing Monte Carlo Simulation Designs
Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Maintained by Phil Chalmers. Last updated 20 hours ago.
monte-carlo-simulationsimulationsimulation-framework
16.6 match 62 stars 13.36 score 253 scripts 46 dependentsyanyachen
MLmetrics:Machine Learning Evaluation Metrics
A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
Maintained by Yachen Yan. Last updated 11 months ago.
19.9 match 69 stars 11.09 score 2.2k scripts 20 dependentsaryanrzn
ATE.ERROR:Estimating ATE with Misclassified Outcomes and Mismeasured Covariates
Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, <doi:10.1177/0962280217743777>; 2019, <doi:10.1002/sim.8073>). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration.
Maintained by Aryan Rezanezhad. Last updated 6 months ago.
56.3 match 3.71 score 16 scriptsmodeloriented
auditor:Model Audit - Verification, Validation, and Error Analysis
Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Maintained by Alicja Gosiewska. Last updated 1 years ago.
classificationerror-analysisexplainable-artificial-intelligencemachine-learningmodel-validationregression-modelsresidualsxai
22.2 match 58 stars 8.76 score 94 scripts 2 dependentsbioc
HEM:Heterogeneous error model for identification of differentially expressed genes under multiple conditions
This package fits heterogeneous error models for analysis of microarray data
Maintained by HyungJun Cho. Last updated 5 months ago.
microarraydifferentialexpression
41.9 match 4.30 score 6 scriptsdchristop
inflection:Finds the Inflection Point of a Curve
Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> . Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> . Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Maintained by Demetris T. Christopoulos. Last updated 3 years ago.
33.9 match 5.30 score 52 scripts 6 dependentstidymodels
yardstick:Tidy Characterizations of Model Performance
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
Maintained by Emil Hvitfeldt. Last updated 4 days ago.
11.4 match 387 stars 15.47 score 2.2k scripts 60 dependentsrobjhyndman
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.
forecastforecastingopenblascpp
8.6 match 1.1k stars 18.63 score 16k scripts 239 dependentsdata-cleaning
errorlocate:Locate Errors with Validation Rules
Errors in data can be located and removed using validation rules from package 'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 7.
Maintained by Edwin de Jonge. Last updated 9 months ago.
data-cleaningerrorsinvalidation
26.1 match 22 stars 6.11 score 59 scriptslindanab
mecor:Measurement Error Correction in Linear Models with a Continuous Outcome
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Maintained by Linda Nab. Last updated 3 years ago.
linear-modelsmeasurement-errorstatistics
31.5 match 6 stars 5.07 score 13 scriptstidyverse
purrr:Functional Programming Tools
A complete and consistent functional programming toolkit for R.
Maintained by Hadley Wickham. Last updated 1 months ago.
7.1 match 1.3k stars 22.12 score 59k scripts 6.9k dependentsstephens999
ashr:Methods for Adaptive Shrinkage, using Empirical Bayes
The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <DOI:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics---estimated effects and standard errors---are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).
Maintained by Peter Carbonetto. Last updated 10 months ago.
12.9 match 82 stars 12.10 score 780 scripts 15 dependentseasystats
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.
aiceasystatshacktoberfestloomachine-learningmixed-modelsmodelsperformancer2statistics
9.6 match 1.1k stars 16.17 score 4.3k scripts 47 dependentsmazamascience
MazamaCoreUtils:Utility Functions for Production R Code
A suite of utility functions providing functionality commonly needed for production level projects such as logging, error handling, cache management and date-time parsing. Functions for date-time parsing and formatting require that time zones be specified explicitly, avoiding a common source of error when working with environmental time series.
Maintained by Jonathan Callahan. Last updated 3 months ago.
19.6 match 4 stars 7.76 score 119 scripts 5 dependentsixpantia
googleErrorReportingR:Send Error Reports to the Google Error Reporting Service API
Send error reports to the Google Error Reporting service <https://cloud.google.com/error-reporting/> and view errors and assign error status in the Google Error Reporting user interface.
Maintained by Frans van Dunné. Last updated 5 months ago.
gcpgcp-error-reportinggooglecloudplatformhacktoberfest
28.0 match 6 stars 5.38 score 8 scriptsmrcieu
TwoSampleMR:Two Sample MR Functions and Interface to MRC Integrative Epidemiology Unit OpenGWAS Database
A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide range of methods to run the analysis.
Maintained by Gibran Hemani. Last updated 11 days ago.
13.1 match 467 stars 11.23 score 1.7k scripts 1 dependentsjackstat
ModelMetrics:Rapid Calculation of Model Metrics
Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
Maintained by Tyler Hunt. Last updated 4 years ago.
aucloglossmachine-learningmetricsmodel-evaluationmodel-metricscpp
12.3 match 29 stars 11.83 score 1.3k scripts 306 dependentstomasfryda
h2o:R Interface for the 'H2O' Scalable Machine Learning Platform
R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Maintained by Tomas Fryda. Last updated 1 years ago.
17.6 match 3 stars 8.20 score 7.8k scripts 11 dependentsbioc
variancePartition:Quantify and interpret drivers of variation in multilevel gene expression experiments
Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures.
Maintained by Gabriel E. Hoffman. Last updated 2 months ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
12.2 match 7 stars 11.69 score 1.1k scripts 3 dependentsepiforecasts
scoringutils:Utilities for Scoring and Assessing Predictions
Facilitate the evaluation of forecasts in a convenient framework based on data.table. It allows user to to check their forecasts and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package mostly focuses on the evaluation of probabilistic forecasts and allows evaluating several different forecast types and input formats. Find more information about the package in the Vignettes as well as in the accompanying paper, <doi:10.48550/arXiv.2205.07090>.
Maintained by Nikos Bosse. Last updated 13 days ago.
forecast-evaluationforecasting
12.3 match 52 stars 11.37 score 326 scripts 7 dependentsthinkr-open
fcuk:The Ultimate Helper for Clumsy Fingers
Automatically suggests a correction when a typo occurs.
Maintained by Vincent Guyader. Last updated 1 years ago.
19.1 match 92 stars 7.05 score 49 scriptsaalfons
perry:Resampling-Based Prediction Error Estimation for Regression Models
Tools that allow developers to write functions for prediction error estimation with minimal programming effort and assist users with model selection in regression problems.
Maintained by Andreas Alfons. Last updated 3 years ago.
27.6 match 2 stars 4.84 score 20 scripts 10 dependentskkholst
mets:Analysis of Multivariate Event Times
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Maintained by Klaus K. Holst. Last updated 3 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
9.8 match 14 stars 13.47 score 236 scripts 42 dependentscrwerner
FieldSimR:Simulation of Plot Errors and Phenotypes in Plant Breeding Field Trials
Simulates plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture spatial trend, random error (noise), and extraneous variation, which are combined at a user-defined ratio. Phenotypes can be generated by combining the plot errors with simulated genetic values that capture genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.
Maintained by Christian Werner. Last updated 2 days ago.
18.5 match 9 stars 7.13 score 62 scriptsemmaskarstein
inlamemi:Missing Data and Measurement Error Modelling in INLA
Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.
Maintained by Emma Skarstein. Last updated 4 months ago.
22.1 match 5.97 score 19 scriptst-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.
12.2 match 10.82 score 10k scripts 54 dependentsohdsi
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 30 days ago.
15.4 match 10 stars 8.51 score 151 scripts 1 dependentsharrelfe
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 9 hours ago.
7.4 match 210 stars 17.61 score 17k scripts 750 dependentsanthonychristidis
RPESE:Estimates of Standard Errors for Risk and Performance Measures
Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.
Maintained by Anthony Christidis. Last updated 7 months ago.
33.8 match 3.83 score 9 scriptscoatless-rpkg
searcher:Query Search Interfaces
Provides a search interface to look up terms on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek', 'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'. Upon searching, a browser window will open with the aforementioned search results.
Maintained by James Balamuta. Last updated 6 months ago.
automaticerror-handlingerror-messagessearch-enginesearch-portals
16.8 match 72 stars 7.69 score 19 scripts 3 dependentscoatless-rpkg
errorist:Automatically Search Errors or Warnings
Provides environment hooks that obtain errors and warnings which occur during the execution of code to automatically search for solutions.
Maintained by James Balamuta. Last updated 1 years ago.
error-handlingerrorsr-programmingsearch-enginewarnings
24.5 match 27 stars 5.24 score 13 scriptsss3sim
ss3sim:Fisheries Stock Assessment Simulation Testing with Stock Synthesis
A framework for fisheries stock assessment simulation testing with Stock Synthesis (SS3) as described in Anderson et al. (2014) <doi:10.1371/journal.pone.0092725>.
Maintained by Kelli F. Johnson. Last updated 5 months ago.
fisheriessimulationstock-synthesis
14.4 match 39 stars 8.89 score 149 scriptsanimint
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 27 days ago.
14.3 match 64 stars 8.87 score 173 scriptsmlcollyer
RRPP:Linear Model Evaluation with Randomized Residuals in a Permutation Procedure
Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Maintained by Michael Collyer. Last updated 26 days ago.
12.8 match 4 stars 9.84 score 173 scripts 7 dependentsellessenne
rsimsum:Analysis of Simulation Studies Including Monte Carlo Error
Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the 'simsum' user-written command in 'Stata' (White I.R., 2010 <https://www.stata-journal.com/article.html?article=st0200>), further extending it with additional performance measures and functionality.
Maintained by Alessandro Gasparini. Last updated 10 months ago.
biostatisticsmonte-carlo-errorsimulationsimulation-studysimulationsstatistics
16.3 match 28 stars 7.70 score 148 scriptsjiscah
sequoia:Pedigree Inference from SNPs
Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.
Maintained by Jisca Huisman. Last updated 9 months ago.
pedigreepedigree-reconstructionpedigreessequoiasnpsnp-datafortran
16.7 match 26 stars 7.40 score 79 scriptslrberge
dreamerr:Error Handling Made Easy
Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.
Maintained by Laurent Berge. Last updated 5 months ago.
13.2 match 27 stars 9.32 score 16 scripts 32 dependentsphilipppro
measures:Performance Measures for Statistical Learning
Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Maintained by Philipp Probst. Last updated 4 years ago.
27.3 match 1 stars 4.47 score 88 scripts 2 dependentskenaho1
asbio:A Collection of Statistical Tools for Biologists
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Maintained by Ken Aho. Last updated 2 months ago.
16.5 match 5 stars 7.32 score 310 scripts 3 dependentshzambran
hydroGOF:Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
Maintained by Mauricio Zambrano-Bigiarini. Last updated 10 months ago.
11.7 match 40 stars 10.29 score 796 scripts 8 dependentsbioc
scde:Single Cell Differential Expression
The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
Maintained by Evan Biederstedt. Last updated 5 months ago.
immunooncologyrnaseqstatisticalmethoddifferentialexpressionbayesiantranscriptionsoftwareanalysisbioinformaticsheterogenityngssingle-celltranscriptomicsopenblascppopenmp
15.9 match 173 stars 7.53 score 141 scriptsdaroczig
logger:A Lightweight, Modern and Flexible Logging Utility
Inspired by the the 'futile.logger' R package and 'logging' Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.
Maintained by Gergely Daróczi. Last updated 2 months ago.
6.9 match 298 stars 16.88 score 1.5k scripts 98 dependentsmatthieustigler
tsDyn:Nonlinear Time Series Models with Regime Switching
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Maintained by Matthieu Stigler. Last updated 5 months ago.
11.0 match 34 stars 10.56 score 684 scripts 3 dependentskbroman
qtl:Tools for Analyzing QTL Experiments
Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Maintained by Karl W Broman. Last updated 7 months ago.
9.1 match 80 stars 12.79 score 2.4k scripts 29 dependentspharmar
riskmetric:Risk Metrics to Evaluating R Packages
Facilities for assessing R packages against a number of metrics to help quantify their robustness.
Maintained by Eli Miller. Last updated 9 days ago.
13.0 match 167 stars 8.89 score 43 scriptsbrry
berryFunctions:Function Collection Related to Plotting and Hydrology
Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.
Maintained by Berry Boessenkool. Last updated 1 months ago.
11.9 match 13 stars 9.43 score 350 scripts 16 dependentscovaruber
sommer:Solving Mixed Model Equations in R
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
average-informationmixed-modelsrcpparmadilloopenblascppopenmp
8.8 match 43 stars 12.70 score 300 scripts 9 dependentsvandomed
pooling:Fit Poolwise Regression Models
Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.
Maintained by Dane R. Van Domelen. Last updated 5 years ago.
assay-modelingbiomarkersefficiencyepidemiologymaximum-likelihoodmeasurement-errorpooling
30.9 match 3.60 score 80 scriptsstatistikat
surveysd:Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs
Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.
Maintained by Johannes Gussenbauer. Last updated 3 months ago.
bootstraperror-estimationsurveycpp
16.2 match 9 stars 6.86 score 67 scriptsconfig-i1
greybox:Toolbox for Model Building and Forecasting
Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
Maintained by Ivan Svetunkov. Last updated 2 days ago.
forecastingmodel-selectionmodel-selection-and-evaluationregressionregression-modelsstatisticscpp
10.1 match 30 stars 11.03 score 97 scripts 34 dependentsalexkowa
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.
8.6 match 26 stars 12.80 score 2.4k scripts 46 dependentsbsvars
bsvars:Bayesian Estimation of Structural Vector Autoregressive Models
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.
Maintained by Tomasz Woźniak. Last updated 1 months ago.
bayesian-inferenceeconometricsvector-autoregressionopenblascppopenmp
14.3 match 46 stars 7.67 score 32 scripts 1 dependentsdgbonett
vcmeta:Varying Coefficient Meta-Analysis
Implements functions for varying coefficient meta-analysis methods. These methods do not assume effect size homogeneity. Subgroup effect size comparisons, general linear effect size contrasts, and linear models of effect sizes based on varying coefficient methods can be used to describe effect size heterogeneity. Varying coefficient meta-analysis methods do not require the unrealistic assumptions of the traditional fixed-effect and random-effects meta-analysis methods. For details see: Statistical Methods for Psychologists, Volume 5, <https://dgbonett.sites.ucsc.edu/>.
Maintained by Douglas G. Bonett. Last updated 8 months ago.
36.2 match 1 stars 3.00 score 8 scriptsdvats
mcmcse:Monte Carlo Standard Errors for MCMC
Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.
Maintained by Dootika Vats. Last updated 1 months ago.
effective-sample-sizemcmcoutput-aopenblascpp
12.2 match 12 stars 8.77 score 314 scripts 17 dependentsthothorn
ipred:Improved Predictors
Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.
Maintained by Torsten Hothorn. Last updated 8 months ago.
9.9 match 10.76 score 3.3k scripts 411 dependentspecanproject
PEcAn.benchmark:PEcAn Functions Used for Benchmarking
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. The PEcAn.benchmark package provides utilities for comparing models and data, including a suite of statistical metrics and plots.
Maintained by Mike Dietze. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplants
9.9 match 216 stars 10.70 score 416 scripts 11 dependentsr-forge
car:Companion to Applied Regression
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.
Maintained by John Fox. Last updated 5 months ago.
6.9 match 15.29 score 43k scripts 901 dependentsnlmixr2
rxode2:Facilities for Simulating from ODE-Based Models
Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.
Maintained by Matthew L. Fidler. Last updated 30 days ago.
9.3 match 40 stars 11.24 score 220 scripts 13 dependentskeaven
gsDesign:Group Sequential Design
Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.
Maintained by Keaven Anderson. Last updated 12 days ago.
biostatisticsboundariesclinical-trialsdesignspending-functions
8.0 match 51 stars 13.05 score 338 scripts 5 dependentsjranke
mkin:Kinetic Evaluation of Chemical Degradation Data
Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models <doi:10.3390/environments6120124>. Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end <doi:10.3390/environments8080071>. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.
Maintained by Johannes Ranke. Last updated 1 months ago.
degradationfocus-kineticskinetic-modelskineticsodeode-model
12.8 match 11 stars 8.18 score 78 scripts 1 dependentsbraverock
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.
6.5 match 222 stars 15.93 score 4.8k scripts 20 dependentsjcpsantiago
sentryR:Send Errors and Messages to Sentry
Unofficial client for 'Sentry' <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the 'Plumber' package.
Maintained by Joao Santiago. Last updated 8 months ago.
error-monitoringsentryshared-lib
20.0 match 41 stars 5.17 score 12 scriptsygeunkim
bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling
Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.
Maintained by Young Geun Kim. Last updated 17 days ago.
bayesianbayesian-econometricsbvareigenforecastingharpybind11pythonrcppeigentime-seriesvector-autoregressioncppopenmp
16.0 match 6 stars 6.42 score 25 scriptswalkerke
tidycensus:Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames
An integrated R interface to several United States Census Bureau APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis.
Maintained by Kyle Walker. Last updated 2 months ago.
7.2 match 647 stars 14.27 score 7.5k scripts 10 dependentstagteam
pec:Prediction Error Curves for Risk Prediction Models in Survival Analysis
Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.
Maintained by Thomas A. Gerds. Last updated 2 years ago.
13.6 match 7.42 score 512 scripts 26 dependentspaul-buerkner
brms:Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
6.0 match 1.3k stars 16.61 score 13k scripts 34 dependentsflujoo
erify:Check Arguments and Generate Readable Error Messages
Provides several validator functions for checking if arguments passed by users have valid types, lengths, etc. and for generating informative and well-formatted error messages in a consistent style. Also provides tools for users to create their own validator functions. The error message style used is adopted from <https://style.tidyverse.org/error-messages.html>.
Maintained by Renfei Mao. Last updated 9 months ago.
16.9 match 7 stars 5.62 score 3 scripts 4 dependentsrenands
RMLPCA:Maximum Likelihood Principal Component Analysis
R implementation of Maximum Likelihood Principal Component Analysis The main idea of this package is to have an alternative way of PCA for subspace modeling that considers measurement errors. More details can be found in Peter D. Wentzell (2009) <doi:10.1016/B978-0-444-64165-6.03029-9>.
Maintained by Renan Santos Barbosa. Last updated 4 years ago.
29.7 match 2 stars 3.15 score 14 scriptsohdsi
ParallelLogger:Support for Parallel Computation, Logging, and Function Automation
Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).
Maintained by Martijn Schuemie. Last updated 6 months ago.
9.8 match 12 stars 9.18 score 87 scripts 11 dependentsbioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
Maintained by Gabriel Hoffman. Last updated 5 months ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
11.0 match 12 stars 8.09 score 128 scriptsjdjohn215
pollster:Calculate Crosstab and Topline Tables of Weighted Survey Data
Calculate common types of tables for weighted survey data. Options include topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as well as summary tables of weighted numeric variables. Optionally, include the margin of error at selected confidence intervals including the design effect. The design effect is calculated as described by Kish (1965) <doi:10.1002/bimj.19680100122> beginning on page 257. Output takes the form of tibbles (simple data frames). This package conveniently handles labelled data, such as that commonly used by 'Stata' and 'SPSS.' Complex survey design is not supported at this time.
Maintained by John D. Johnson. Last updated 2 years ago.
15.3 match 9 stars 5.80 score 47 scriptskingaa
pomp:Statistical Inference for Partially Observed Markov Processes
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Maintained by Aaron A. King. Last updated 1 months ago.
abcb-splinedifferential-equationsdynamical-systemsiterated-filteringlikelihoodlikelihood-freemarkov-chain-monte-carlomarkov-modelmathematical-modellingmeasurement-errorparticle-filtersequential-monte-carlosimulation-based-inferencesobol-sequencestate-spacestatistical-inferencestochastic-processestime-seriesopenblas
7.5 match 115 stars 11.81 score 1.3k scripts 4 dependentsdevopifex
erratum:Handle Error and Warning Messages
Elegantly handle error and warning messages.
Maintained by John Coene. Last updated 1 years ago.
29.0 match 22 stars 3.04 score 5 scriptsalexanderlynl
safestats:Safe Anytime-Valid Inference
Functions to design and apply tests that are anytime valid. The functions can be used to design hypothesis tests in the prospective/randomised control trial setting or in the observational/retrospective setting. The resulting tests remain valid under both optional stopping and optional continuation. The current version includes safe t-tests and safe tests of two proportions. For details on the theory of safe tests, see Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021) "Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond" <arXiv:2106.02693> for details on safe contingency table tests.
Maintained by Alexander Ly. Last updated 2 years ago.
evalueshacktoberfestsafe-testingstatistics
16.8 match 6 stars 5.23 score 14 scriptspharmaverse
sdtmchecks:Data Quality Checks for Study Data Tabulation Model (SDTM) Datasets
A series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis.
Maintained by Will Harris. Last updated 3 months ago.
11.4 match 21 stars 7.66 score 15 scriptsstan-dev
posterior:Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
Maintained by Paul-Christian Bürkner. Last updated 10 days ago.
5.3 match 168 stars 16.13 score 3.3k scripts 342 dependentsegenn
rtemis:Machine Learning and Visualization
Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.
Maintained by E.D. Gennatas. Last updated 1 months ago.
data-sciencedata-visualizationmachine-learningmachine-learning-libraryvisualization
12.1 match 145 stars 7.09 score 50 scripts 2 dependentstroutinthemilk
IsotopeR:Stable Isotope Mixing Model
Estimates diet contributions from isotopic sources using JAGS. Includes estimation of concentration dependence and measurement error.
Maintained by Jake Ferguson. Last updated 9 years ago.
31.6 match 1 stars 2.70 scorecardiomoon
interpretCI:Estimate the Confidence Interval and Interpret Step by Step
Estimate confidence intervals for mean, proportion, mean difference for unpaired and paired samples and proportion difference. Plot the confidence intervals. Generate documents explaining the statistical result step by step.
Maintained by Keon-Woong Moon. Last updated 3 years ago.
14.1 match 4 stars 6.03 score 49 scriptsnredell
forecastML:Time Series Forecasting with Machine Learning Methods
The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Maintained by Nickalus Redell. Last updated 5 years ago.
deep-learningdirect-forecastingforecastforecastingmachine-learningmulti-step-ahead-forecastingneural-networkpythontime-series
11.0 match 131 stars 7.64 score 134 scriptsmaarten14c
rice:Radiocarbon Equations
Provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon realms (C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package complements the data package 'rintcal'.
Maintained by Maarten Blaauw. Last updated 2 months ago.
13.7 match 1 stars 6.13 score 13 scripts 4 dependentsbioc
onlineFDR:Online error rate control
This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions.
Maintained by David S. Robertson. Last updated 5 months ago.
multiplecomparisonsoftwarestatisticalmethoderror-rate-controlfdrfwerhypothesis-testingcpp
12.1 match 14 stars 6.88 score 26 scriptscran
airGR:Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references.
Maintained by Olivier Delaigue. Last updated 1 years ago.
12.3 match 4 stars 6.60 score 164 scripts 4 dependentsbioc
BiocParallel:Bioconductor facilities for parallel evaluation
This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects.
Maintained by Martin Morgan. Last updated 26 days ago.
infrastructurebioconductor-packagecore-packageu24ca289073cpp
4.7 match 67 stars 17.40 score 7.3k scripts 1.1k dependentsrstudio
plumber:An API Generator for R
Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.
Maintained by Barret Schloerke. Last updated 5 days ago.
5.6 match 1.4k stars 14.47 score 2.2k scripts 16 dependentsr-lib
httr:Tools for Working with URLs and HTTP
Useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on).
Maintained by Hadley Wickham. Last updated 1 years ago.
3.9 match 989 stars 20.56 score 29k scripts 4.3k dependentskogalur
randomForestSRC:Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
Maintained by Udaya B. Kogalur. Last updated 2 months ago.
10.1 match 10 stars 7.90 score 1.2k scripts 12 dependentshannameyer
CAST:'caret' Applications for Spatial-Temporal Models
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>; Schumacher et al. (2024) <doi:10.5194/egusphere-2024-2730>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.
Maintained by Hanna Meyer. Last updated 2 months ago.
autocorrelationcaretfeature-selectionmachine-learningoverfittingpredictive-modelingspatialspatio-temporalvariable-selection
6.6 match 114 stars 11.97 score 298 scripts 1 dependentscbielow
PTXQC:Quality Report Generation for MaxQuant and mzTab Results
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool). Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.
Maintained by Chris Bielow. Last updated 1 years ago.
drag-and-drophacktoberfestheatmapmatch-between-runsmaxquantmetricmztabopenmsproteomicsquality-controlquality-metricsreport
8.4 match 42 stars 9.35 score 105 scripts 1 dependentscran
sae:Small Area Estimation
Functions for small area estimation.
Maintained by Yolanda Marhuenda. Last updated 5 years ago.
14.1 match 6 stars 5.49 score 83 scripts 8 dependentsrebeccasalles
TSPred:Functions for Benchmarking Time Series Prediction
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Maintained by Rebecca Pontes Salles. Last updated 4 years ago.
benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction
13.8 match 24 stars 5.53 score 94 scripts 1 dependentsoobianom
quickcode:Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.
Maintained by Obinna Obianom. Last updated 14 days ago.
9.8 match 5 stars 7.76 score 7 scripts 6 dependentsnicholasjclark
mvgam:Multivariate (Dynamic) Generalized Additive Models
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Maintained by Nicholas J Clark. Last updated 20 hours ago.
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregressioncpp
7.7 match 139 stars 9.85 score 117 scriptsielbadisy
mcstatsim:Monte Carlo Statistical Simulation Tools Using a Functional Approach
A lightweight package designed to facilitate statistical simulations through functional programming. It centralizes the simulation process into a single higher-order function, enhancing manageability and usability without adding overhead from external dependencies. The package includes ready-to-use functions for common simulation targets. A detailed example can be found on <https://github.com/ielbadisy/mcstatsim>.
Maintained by Imad EL BADISY. Last updated 7 months ago.
21.3 match 1 stars 3.54 score 7 scriptscran
fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Analyze and model heteroskedastic behavior in financial time series.
Maintained by Georgi N. Boshnakov. Last updated 12 months ago.
9.2 match 6 stars 8.20 score 1.1k scripts 51 dependentshwborchers
pracma:Practical Numerical Math Functions
Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.
Maintained by Hans W. Borchers. Last updated 1 years ago.
6.0 match 29 stars 12.34 score 6.6k scripts 931 dependentsrstudio
promises:Abstractions for Promise-Based Asynchronous Programming
Provides fundamental abstractions for doing asynchronous programming in R using promises. Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to 'JavaScript' promises, but with a syntax that is idiomatic R.
Maintained by Joe Cheng. Last updated 1 months ago.
4.3 match 204 stars 17.10 score 688 scripts 2.6k dependentsmwheymans
miceafter:Data and Statistical Analyses after Multiple Imputation
Statistical Analyses and Pooling after Multiple Imputation. A large variety of repeated statistical analysis can be performed and finally pooled. Statistical analysis that are available are, among others, Levene's test, Odds and Risk Ratios, One sample proportions, difference between proportions and linear and logistic regression models. Functions can also be used in combination with the Pipe operator. More and more statistical analyses and pooling functions will be added over time. Heymans (2007) <doi:10.1186/1471-2288-7-33>. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>. Sidi (2021) <doi:10.1080/00031305.2021.1898468>. Lott (2018) <doi:10.1080/00031305.2018.1473796>. Grund (2021) <doi:10.31234/osf.io/d459g>.
Maintained by Martijn Heymans. Last updated 2 years ago.
15.3 match 2 stars 4.84 score 23 scriptsbusiness-science
modeltime:The Tidymodels Extension for Time Series Modeling
The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).
Maintained by Matt Dancho. Last updated 5 months ago.
arimadata-sciencedeep-learningetsforecastingmachine-learningmachine-learning-algorithmsmodeltimeprophettbatstidymodelingtidymodelstimetime-seriestime-series-analysistimeseriestimeseries-forecasting
6.9 match 549 stars 10.57 score 1.1k scripts 7 dependentsben519
mltools:Machine Learning Tools
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Maintained by Ben Gorman. Last updated 3 years ago.
exploratory-data-analysismachine-learning
7.5 match 72 stars 9.58 score 1.2k scripts 13 dependentspharmaverse
admiral:ADaM in R Asset Library
A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).
Maintained by Ben Straub. Last updated 4 days ago.
cdiscclinical-trialsopen-source
5.2 match 236 stars 13.89 score 486 scripts 4 dependentsumr-amap
BIOMASS:Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests
Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct height-diameter models, (4) manage tree and plot coordinates, (5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. To cite 'BIOMASS', please use citation("BIOMASS"). See more in the article of Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.
Maintained by Dominique Lamonica. Last updated 2 days ago.
7.2 match 26 stars 9.90 score 68 scripts 1 dependentsjamiemkass
ENMeval:Automated Tuning and Evaluations of Ecological Niche Models
Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.
Maintained by Jamie M. Kass. Last updated 2 months ago.
6.3 match 49 stars 11.25 score 332 scripts 2 dependentsstatnet
statnet.common:Common R Scripts and Utilities Used by the Statnet Project Software
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
Maintained by Pavel N. Krivitsky. Last updated 27 days ago.
6.2 match 8 stars 11.42 score 197 scripts 148 dependentsradiant-rstats
radiant.data:Data Menu for Radiant: Business Analytics using R and Shiny
The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.
Maintained by Vincent Nijs. Last updated 5 months ago.
8.5 match 54 stars 8.30 score 146 scripts 6 dependentskosukeimai
MatchIt:Nonparametric Preprocessing for Parametric Causal Inference
Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)
Maintained by Noah Greifer. Last updated 2 days ago.
4.7 match 220 stars 15.03 score 2.4k scripts 21 dependentsarcaldwell49
Superpower:Simulation-Based Power Analysis for Factorial Designs
Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.
Maintained by Aaron Caldwell. Last updated 3 months ago.
7.8 match 66 stars 9.02 score 106 scripts 1 dependentsconfig-i1
smooth:Forecasting Using State Space Models
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).
Maintained by Ivan Svetunkov. Last updated 2 days ago.
arimaarima-forecastingcesetsexponential-smoothingforecaststate-spacetime-seriesopenblascpp
5.8 match 90 stars 11.87 score 412 scripts 25 dependentschaoranhu
smam:Statistical Modeling of Animal Movements
Animal movement models including Moving-Resting Process with Embedded Brownian Motion (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>; Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>), Brownian Motion with Measurement Error (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>), Moving-Resting-Handling Process with Embedded Brownian Motion (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>), Moving-Resting Process with Measurement Error (Hu et al., 2021, <doi:10.1111/2041-210X.13694>), Moving-Moving Process with two Embedded Brownian Motions.
Maintained by Chaoran Hu. Last updated 1 years ago.
animal-movementbrownian-motionhidden-markov-modelhidden-statesmeasurement-errortelegraph-processgslcpp
15.3 match 3 stars 4.52 score 11 scriptsgiscience-fsu
sperrorest:Perform Spatial Error Estimation and Variable Importance Assessment
Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.
Maintained by Alexander Brenning. Last updated 2 years ago.
cross-validationmachine-learningspatial-statisticsspatio-temporal-modelingstatistical-learning
10.7 match 19 stars 6.46 score 46 scriptsr-lib
vctrs:Vector Helpers
Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analysing function interfaces.
Maintained by Davis Vaughan. Last updated 5 months ago.
3.6 match 290 stars 18.97 score 1.1k scripts 13k dependentshenrikbengtsson
R.oo:R Object-Oriented Programming with or without References
Methods and classes for object-oriented programming in R with or without references. Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers. The package has been developed since 2001 and is now considered very stable. This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
Maintained by Henrik Bengtsson. Last updated 5 months ago.
5.9 match 20 stars 11.49 score 329 scripts 828 dependentsratihrodliyah
saeHB.ME.beta:SAE with Measurement Error using HB under Beta Distribution
Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
Maintained by Ratih Rodliyah. Last updated 2 years ago.
18.4 match 3.70 score 3 scriptsaphalo
photobiology:Photobiological Calculations
Definitions of classes, methods, operators and functions for use in photobiology and radiation meteorology and climatology. Calculation of effective (weighted) and not-weighted irradiances/doses, fluence rates, transmittance, reflectance, absorptance, absorbance and diverse ratios and other derived quantities from spectral data. Local maxima and minima: peaks, valleys and spikes. Conversion between energy-and photon-based units. Wavelength interpolation. Astronomical calculations related solar angles and day length. Colours and vision. This package is part of the 'r4photobiology' suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Maintained by Pedro J. Aphalo. Last updated 3 days ago.
lightphotobiologyquantificationr4photobiology-suiteradiationspectrasun-position
7.3 match 4 stars 9.35 score 604 scripts 12 dependentsr-lib
cli:Helpers for Developing Command Line Interfaces
A suite of tools to build attractive command line interfaces ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs, etc. Supports custom themes via a 'CSS'-like language. It also contains a number of lower level 'CLI' elements: rules, boxes, trees, and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI colors and text styles as well.
Maintained by Gábor Csárdi. Last updated 2 days ago.
3.5 match 664 stars 19.33 score 1.4k scripts 14k dependentsdmurdoch
plotrix:Various Plotting Functions
Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.
Maintained by Duncan Murdoch. Last updated 1 years ago.
5.9 match 5 stars 11.31 score 9.2k scripts 361 dependentsjfwambaugh
invivoPKfit:Fits Toxicokinetic Models to In Vivo PK Data Sets
Takes in vivo toxicokinetic concentration-time data and fits parameters of 1-compartment and 2-compartment models for each chemical. These methods are described in detail in "Informatics for Toxicokinetics" (submitted).
Maintained by John Wambaugh. Last updated 2 months ago.
25.7 match 2.60 score 4 scriptsluca-scr
mclust:Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Maintained by Luca Scrucca. Last updated 11 months ago.
5.5 match 21 stars 12.23 score 6.6k scripts 587 dependentspoissonconsulting
err:Customizable Object Sensitive Messages
Messages should provide users with readable information about R objects without flooding their console. 'cc()' concatenates vector and data frame values into a grammatically correct string using commas, an ellipsis and conjunction. 'cn()' allows the user to define a string which varies based on a count. 'co()' combines the two to produce a customizable object aware string. The package further facilitates this process by providing five 'sprintf'-like types such as '%n' for the length of an object and '%o' for its name as well as wrappers for pasting objects and issuing errors, warnings and messages.
Maintained by Joe Thorley. Last updated 2 months ago.
13.8 match 7 stars 4.80 score 50 scripts 2 dependentsgavinsimpson
analogue:Analogue and Weighted Averaging Methods for Palaeoecology
Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.
Maintained by Gavin L. Simpson. Last updated 6 months ago.
7.3 match 14 stars 8.96 score 185 scripts 4 dependentslaresbernardo
lares:Analytics & Machine Learning Sidekick
Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.
Maintained by Bernardo Lares. Last updated 24 days ago.
analyticsapiautomationautomldata-sciencedescriptive-statisticsh2omachine-learningmarketingmmmpredictive-modelingpuzzlerlanguagerobynvisualization
6.6 match 233 stars 9.84 score 185 scripts 1 dependentslebebr01
simglm:Simulate Models Based on the Generalized Linear Model
Simulates regression models, including both simple regression and generalized linear mixed models with up to three level of nesting. Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package.
Maintained by Brandon LeBeau. Last updated 10 months ago.
8.2 match 43 stars 7.87 score 87 scriptsdementiy
vkR:Access to VK API via R
Provides an interface to the VK API <https://vk.com/dev/methods>. VK <https://vk.com/> is the largest European online social networking service, based in Russia.
Maintained by Dmitriy Sorokin. Last updated 4 years ago.
12.0 match 56 stars 5.36 score 41 scriptstherneau
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.
3.1 match 400 stars 20.43 score 29k scripts 3.9k dependentsalanarnholt
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.
6.8 match 7 stars 9.11 score 1.3k scripts 6 dependentss3alfisc
summclust:Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors
Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <arXiv:2205.03288v1>).
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorsfixestlinear-regressionrobust-inference
10.0 match 6 stars 6.16 score 53 scripts 3 dependentssingmann
afex:Analysis of Factorial Experiments
Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).
Maintained by Henrik Singmann. Last updated 7 months ago.
4.2 match 123 stars 14.50 score 1.4k scripts 15 dependentsadrian-bowman
sm:Smoothing Methods for Nonparametric Regression and Density Estimation
This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Maintained by Adrian Bowman. Last updated 1 years ago.
8.7 match 1 stars 6.99 score 732 scripts 36 dependentsrdatatable
data.table:Extension of `data.frame`
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
Maintained by Tyson Barrett. Last updated 2 days ago.
2.6 match 3.7k stars 23.53 score 230k scripts 4.6k dependentsdcgerard
updog:Flexible Genotyping for Polyploids
Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.
Maintained by David Gerard. Last updated 1 years ago.
7.1 match 28 stars 8.45 score 83 scripts 2 dependentspsirusteam
samplesize4surveys:Sample Size Calculations for Complex Surveys
Computes the required sample size for estimation of totals, means and proportions under complex sampling designs.
Maintained by Hugo Andres Gutierrez Rojas. Last updated 5 years ago.
12.5 match 2 stars 4.78 score 60 scriptscanmod
macpan2:Fast and Flexible Compartmental Modelling
Fast and flexible compartmental modelling with Template Model Builder.
Maintained by Steve Walker. Last updated 2 days ago.
compartmental-modelsepidemiologyforecastingmixed-effectsmodel-fittingoptimizationsimulationsimulation-modelingcpp
6.6 match 4 stars 8.89 score 246 scripts 1 dependentsaryoda
tryCatchLog:Advanced 'tryCatch()' and 'try()' Functions
Advanced tryCatch() and try() functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).
Maintained by Juergen Altfeld. Last updated 2 years ago.
6.8 match 73 stars 8.63 score 116 scripts 9 dependentsmazamascience
beakr:A Minimalist Web Framework for R
A minimalist web framework for developing application programming interfaces in R that provides a flexible framework for handling common HTTP-requests, errors, logging, and an ability to integrate any R code as server middle-ware.
Maintained by Jonathan Callahan. Last updated 2 years ago.
10.1 match 95 stars 5.81 score 34 scriptsskranz
restorepoint:Debugging with Restore Points
Debugging with restore points instead of break points. A restore point stores all local variables when called inside a function. The stored values can later be retrieved and evaluated in a modified R console that replicates the function's environment. To debug step by step, one can simply copy & paste the function body from the R script. Particularly convenient in combination with "RStudio". See the "Github" page inst/vignettes for a tutorial.
Maintained by Roman Zenka. Last updated 9 months ago.
9.4 match 16 stars 6.20 score 79 scripts 42 dependentspecanproject
PEcAn.utils:PEcAn Functions Used for Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by Rob Kooper. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplants
5.3 match 216 stars 10.92 score 218 scripts 35 dependentsiame-researchcenter
PFIM:Population Fisher Information Matrix
Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
Maintained by Romain Leroux. Last updated 5 months ago.
20.9 match 2.78 score 9 scriptsdsy109
mixtools:Tools for Analyzing Finite Mixture Models
Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).
Maintained by Derek Young. Last updated 9 months ago.
mixture-modelsmixture-of-expertssemiparametric-regression
5.1 match 20 stars 11.34 score 1.4k scripts 56 dependentswjbraun
DAAG:Data Analysis and Graphics Data and Functions
Functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.
Maintained by W. John Braun. Last updated 11 months ago.
7.0 match 8.25 score 1.2k scripts 1 dependentsmlverse
luz:Higher Level 'API' for 'torch'
A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
Maintained by Daniel Falbel. Last updated 6 months ago.
5.9 match 89 stars 9.86 score 318 scripts 4 dependentsmllg
batchtools:Tools for Computation on Batch Systems
As a successor of the packages 'BatchJobs' and 'BatchExperiments', this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers 'IBM Spectrum LSF' (<https://www.ibm.com/products/hpc-workload-management>), 'OpenLava' (<https://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid Engine' (<https://www.univa.com/>), 'Slurm' (<https://slurm.schedmd.com/>), 'TORQUE/PBS' (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>), or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.
Maintained by Michel Lang. Last updated 2 years ago.
batchexperimentsbatchjobsdocker-swarmhigh-performance-computinghpchpc-clusterslsfopenlavaparallel-computingreproducibilitysgeslurmtorque
5.1 match 175 stars 11.39 score 772 scripts 14 dependentsbayesiandemography
bage:Bayesian Estimation and Forecasting of Age-Specific Rates
Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'.
Maintained by John Bryant. Last updated 2 months ago.
7.9 match 3 stars 7.30 score 39 scriptsjbytecode
eive:An Algorithm for Reducing Errors-in-Variable Bias in Simple and Multiple Linear Regressions
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.
Maintained by Mehmet Hakan Satman. Last updated 2 years ago.
compact-genetic-algorithmerrors-in-variableslinear-regressioncpp
21.4 match 1 stars 2.70 score 6 scriptsmightymetrika
npboottprm:Nonparametric Bootstrap Test with Pooled Resampling
Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration.
Maintained by Mackson Ncube. Last updated 6 months ago.
datasciencenonparametricstatistics
13.3 match 1 stars 4.32 score 5 scripts 2 dependentsbioc
biodb:biodb, a library and a development framework for connecting to chemical and biological databases
The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages.
Maintained by Pierrick Roger. Last updated 5 months ago.
softwareinfrastructuredataimportkeggbiologycheminformaticschemistrydatabasescpp
7.3 match 11 stars 7.85 score 24 scripts 6 dependentsanthonydevaux
DynForest:Random Forest with Multivariate Longitudinal Predictors
Based on random forest principle, 'DynForest' is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) <doi: 10.1177/09622802231206477>.
Maintained by Anthony Devaux. Last updated 5 months ago.
9.0 match 16 stars 6.38 score 8 scriptsfbertran
peperr:Parallelised Estimation of Prediction Error
Designed for prediction error estimation through resampling techniques, possibly accelerated by parallel execution on a compute cluster. Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062> and were used, for instance, in Porzelius Ch., Schumacher M.and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.
Maintained by Frederic Bertrand. Last updated 3 years ago.
13.0 match 2 stars 4.38 score 20 scripts 1 dependentscorentinjgosling
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.
17.9 match 3.18 score 3 scriptsjinghuazhao
gap:Genetic Analysis Package
As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).
Maintained by Jing Hua Zhao. Last updated 16 days ago.
4.8 match 12 stars 11.88 score 448 scripts 16 dependentscalvagone
campsismod:Generic Implementation of a PK/PD Model
A generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read/write a pharmacometric model from/to files and adapt it further on the fly in the R environment. For this purpose, this package provides an intuitive API to add, modify or delete equations, ordinary differential equations (ODE's), model parameters or compartment properties (like infusion duration or rate, bioavailability and initial values). Finally, this package also provides a useful export of the model for use with simulation packages 'rxode2' and 'mrgsolve'. This package is designed and intended to be used with package 'campsis', a PK/PD simulation platform built on top of 'rxode2' and 'mrgsolve'.
Maintained by Nicolas Luyckx. Last updated 1 months ago.
8.5 match 5 stars 6.64 score 42 scripts 1 dependentsdataoneorg
dataone:R Interface to the DataONE REST API
Provides read and write access to data and metadata from the DataONE network <https://www.dataone.org> of data repositories. Each DataONE repository implements a consistent repository application programming interface. Users call methods in R to access these remote repository functions, such as methods to query the metadata catalog, get access to metadata for particular data packages, and read the data objects from the data repository. Users can also insert and update data objects on repositories that support these methods.
Maintained by Matthew B. Jones. Last updated 3 years ago.
5.7 match 36 stars 9.93 score 472 scripts 3 dependentsdfe-analytical-services
shinyGovstyle:Custom Gov Style Inputs for Shiny
Collection of 'shiny' application styling that are the based on the GOV.UK Design System. See <https://design-system.service.gov.uk/components/> for details.
Maintained by Ross Wyatt. Last updated 2 days ago.
8.4 match 44 stars 6.69 score 25 scriptsngreifer
WeightIt:Weighting for Covariate Balance in Observational Studies
Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.
Maintained by Noah Greifer. Last updated 5 days ago.
causal-inferenceinverse-probability-weightsobservational-studypropensity-scores
4.8 match 112 stars 11.58 score 508 scripts 3 dependentsvegandevs
vegan:Community Ecology Package
Ordination methods, diversity analysis and other functions for community and vegetation ecologists.
Maintained by Jari Oksanen. Last updated 16 days ago.
ecological-modellingecologyordinationfortranopenblas
2.9 match 472 stars 19.41 score 15k scripts 440 dependentsbioc
safe:Significance Analysis of Function and Expression
SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
differentialexpressionpathwaysgenesetenrichmentstatisticalmethodsoftware
10.0 match 5.60 score 32 scripts 5 dependentsmlr-org
mlr3proba:Probabilistic Supervised Learning for 'mlr3'
Provides extensions for probabilistic supervised learning for 'mlr3'. This includes extending the regression task to probabilistic and interval regression, adding a survival task, and other specialized models, predictions, and measures.
Maintained by John Zobolas. Last updated 2 months ago.
density-estimationmachine-learningmlr3probabilistic-regressionprobabilistic-supervised-learningsupervised-learningsurvival-analysiscpp
7.1 match 135 stars 7.78 score 246 scriptsashenoy-cmbi
grafify:Easy Graphs for Data Visualisation and Linear Models for ANOVA
Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.
Maintained by Avinash R Shenoy. Last updated 1 days ago.
ggplot2linear-modelspost-hoc-comparisonsstatisticsvignettes
10.4 match 48 stars 5.31 score 107 scriptshofnerb
stabs:Stability Selection with Error Control
Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are implemented. The package can be combined with arbitrary user specified variable selection approaches.
Maintained by Benjamin Hofner. Last updated 4 years ago.
machine-learningr-languageresamplingstability-selectionvariable-importancevariable-selection
5.7 match 26 stars 9.59 score 53 scripts 31 dependentshadley
assertthat:Easy Pre and Post Assertions
An extension to stopifnot() that makes it easy to declare the pre and post conditions that you code should satisfy, while also producing friendly error messages so that your users know what's gone wrong.
Maintained by Hadley Wickham. Last updated 6 years ago.
3.5 match 207 stars 15.21 score 2.5k scripts 984 dependentsosorensen
hdme:High-Dimensional Regression with Measurement Error
Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
Maintained by Oystein Sorensen. Last updated 2 years ago.
10.5 match 8 stars 5.08 score 30 scriptsjamesramsay5
fda:Functional Data Analysis
These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from <https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Maintained by James Ramsay. Last updated 4 months ago.
4.3 match 3 stars 12.29 score 2.0k scripts 143 dependentsdaattali
shinydisconnect:Show a Nice Message When a 'Shiny' App Disconnects or Errors
A 'Shiny' app can disconnect for a variety of reasons: an unrecoverable error occurred in the app, the server went down, the user lost internet connection, or any other reason that might cause the 'Shiny' app to lose connection to its server. With 'shinydisconnect', you can call disonnectMessage() anywhere in a Shiny app's UI to add a nice message when this happens. Works locally (running Shiny apps within 'RStudio') and on Shiny servers (such as shinyapps.io, 'RStudio Connect', 'Shiny Server Open Source', 'Shiny Server Pro'). See demo online at <https://daattali.com/shiny/shinydisconnect-demo/>.
Maintained by Dean Attali. Last updated 7 months ago.
6.8 match 65 stars 7.80 score 107 scripts 7 dependentsoscarkjell
text:Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning
Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.
Maintained by Oscar Kjell. Last updated 4 days ago.
deep-learningmachine-learningnlptransformersopenjdk
4.0 match 146 stars 13.16 score 436 scripts 1 dependentssmac-group
navigation:Analyze the Impact of Sensor Error Modelling on Navigation Performance
Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) <doi:10.1109/TIM.2023.3267360> which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions.
Maintained by Lionel Voirol. Last updated 2 years ago.
10.7 match 5 stars 4.95 score 18 scriptscran
Monte.Carlo.se:Monte Carlo Standard Errors
Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.
Maintained by Dennis Boos. Last updated 2 years ago.
20.3 match 2.60 scoresthawinke
oosse:Out-of-Sample R² with Standard Error Estimation
Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.
Maintained by Stijn Hawinkel. Last updated 6 months ago.
12.3 match 4 stars 4.30 score 5 scriptsrich-iannone
messaging:Conveniently Issue Messages, Warnings, and Errors
Provides tools for creating and issuing nicely-formatted text within R diagnostic messages and those messages given during warnings and errors. The formatting of the messages can be customized using templating features. Issues with singular and plural forms can be handled through specialized syntax.
Maintained by Richard Iannone. Last updated 7 years ago.
errorsfunctionsmessageswarnings
13.5 match 12 stars 3.86 score 12 scriptsbusiness-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.
anomalyanomaly-detectiondecompositiondetect-anomaliesiqrtime-series
5.5 match 339 stars 9.56 score 332 scriptsalexanderrobitzsch
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-theoryopenblascpp
5.2 match 23 stars 10.01 score 280 scripts 22 dependentscran
futile.logger:A Logging Utility for R
Provides a simple yet powerful logging utility. Based loosely on log4j, futile.logger takes advantage of R idioms to make logging a convenient and easy to use replacement for cat and print statements.
Maintained by Brian Lee Yung Rowe. Last updated 9 years ago.
5.6 match 9.32 score 1.7k scripts 1.2k dependentsberndbischl
BBmisc:Miscellaneous Helper Functions for B. Bischl
Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development.
Maintained by Bernd Bischl. Last updated 2 years ago.
4.9 match 20 stars 10.59 score 980 scripts 69 dependents