Showing 48 of total 48 results (show query)
tidymodels
broom:Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Maintained by Simon Couch. Last updated 7 hours ago.
1.5k stars 21.58 score 37k scripts 1.5k dependents
rstudio
bslib:Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'
Simplifies custom 'CSS' styling of both 'shiny' and 'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as well as their various 'Bootswatch' themes. An interactive widget is also provided for previewing themes in real time.
Maintained by Carson Sievert. Last updated 23 days ago.
bootstraphtmltoolsrmarkdownsassshiny
511 stars 18.02 score 5.1k scripts 4.3k dependents
tidyverse
modelr:Modelling Functions that Work with the Pipe
Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.
Maintained by Hadley Wickham. Last updated 1 years ago.
400 stars 16.46 score 6.9k scripts 1.1k dependents
kkholst
lava:Latent Variable Models
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Maintained by Klaus K. Holst. Last updated 3 months ago.
latent-variable-modelssimulationstatisticsstructural-equation-models
33 stars 12.87 score 610 scripts 478 dependents
csgillespie
poweRlaw:Analysis of Heavy Tailed Distributions
An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.
Maintained by Colin Gillespie. Last updated 2 months ago.
112 stars 12.79 score 332 scripts 32 dependents
reconhub
incidence:Compute, Handle, Plot and Model Incidence of Dated Events
Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Maintained by Tim Taylor. Last updated 8 months ago.
58 stars 12.06 score 504 scripts 11 dependents
gavinsimpson
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.
14 stars 8.87 score 185 scripts 4 dependents
gmgeorg
LambertW:Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data
Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.
Maintained by Georg M. Goerg. Last updated 1 years ago.
gaussianizegaussianize-dataheavy-tailedheavy-tailed-distributionsleptokurtosisnormal-distributionnormalizationskewed-datastatisticscpp
10 stars 8.16 score 78 scripts 13 dependents
aloy
lmeresampler:Bootstrap Methods for Nested Linear Mixed-Effects Models
Bootstrap routines for nested linear mixed effects models fit using either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>, and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.
Maintained by Adam Loy. Last updated 1 years ago.
37 stars 7.83 score 102 scripts 3 dependents
scottkosty
bootstrap:Functions for the Book "An Introduction to the Bootstrap"
Software (bootstrap, cross-validation, jackknife) and data for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package "boot".
Maintained by Scott Kostyshak. Last updated 6 years ago.
7.62 score 890 scripts 30 dependents
amvillegas
StMoMo:Stochastic Mortality Modelling
Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.
Maintained by Andres Villegas. Last updated 5 years ago.
23 stars 7.54 score 84 scripts 3 dependents
kenaho1
asbio:A Collection of Statistical Tools for Biologists
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Maintained by Ken Aho. Last updated 2 months ago.
5 stars 7.32 score 310 scripts 3 dependents
egenn
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 2 months ago.
data-sciencedata-visualizationmachine-learningmachine-learning-libraryvisualization
145 stars 7.09 score 50 scripts 2 dependents
sachaepskamp
psychonetrics:Structural Equation Modeling and Confirmatory Network Analysis
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Maintained by Sacha Epskamp. Last updated 2 days ago.
51 stars 6.88 score 41 scripts 1 dependents
kingaa
ouch:Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Maintained by Aaron A. King. Last updated 5 months ago.
adaptive-regimebrownian-motionornstein-uhlenbeckornstein-uhlenbeck-modelsouchphylogenetic-comparative-hypothesesphylogenetic-comparative-methodsphylogenetic-datareact
15 stars 6.87 score 68 scripts 4 dependents
bioc
mnem:Mixture Nested Effects Models
Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.
Maintained by Martin Pirkl. Last updated 2 days ago.
pathwayssystemsbiologynetworkinferencenetworkrnaseqpooledscreenssinglecellcrispratacseqdnaseqgeneexpressioncpp
4 stars 6.81 score 15 scripts 4 dependents
talbano
equate:Observed-Score Linking and Equating
Contains methods for observed-score linking and equating under the single-group, equivalent-groups, and nonequivalent-groups with anchor test(s) designs. Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided.
Maintained by Anthony Albano. Last updated 2 years ago.
9 stars 6.72 score 31 scripts 3 dependents
sonsoleslp
tna:Transition Network Analysis (TNA)
Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.
Maintained by Sonsoles López-Pernas. Last updated 2 days ago.
educational-data-mininglearning-analyticsmarkov-modeltemporal-analysis
4 stars 6.51 score 5 scripts
mikemeredith
overlap:Estimates of Coefficient of Overlapping for Animal Activity Patterns
Provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals.
Maintained by Liz Campbell. Last updated 2 years ago.
2 stars 6.38 score 265 scripts 1 dependentsbioc
structToolbox:Data processing & analysis tools for Metabolomics and other omics
An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.
Maintained by Gavin Rhys Lloyd. Last updated 1 months ago.
workflowstepmetabolomicsbioconductor-packagedimslc-msmachine-learningmultivariate-analysisstatisticsunivariate
10 stars 6.26 score 12 scripts
krisrs1128
multimedia:Multimodal Mediation Analysis
Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.
Maintained by Kris Sankaran. Last updated 1 months ago.
coveragemicrobiomeregressionsequencingsoftwarestatisticalmethodstructuralequationmodelscausal-inferencedata-integrationmediation-analysis
1 stars 5.49 score 13 scripts
albertofranzin
bnstruct:Bayesian Network Structure Learning from Data with Missing Values
Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.
Maintained by Alberto Franzin. Last updated 1 years ago.
1 stars 5.40 score 111 scripts 3 dependents
maximeherve
RVAideMemoire:Testing and Plotting Procedures for Biostatistics
Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.
Maintained by Maxime HERVE. Last updated 1 years ago.
8 stars 5.31 score 632 scripts
psolymos
detect:Analyzing Wildlife Data with Detection Error
Models for analyzing site occupancy and count data models with detection error, including single-visit based models (Lele et al. 2012 <doi:10.1093/jpe/rtr042>, Moreno et al. 2010 <doi:10.1890/09-1073.1>, Solymos et al. 2012 <doi:10.1002/env.1149>, Denes et al. 2016 <doi:10.1111/1365-2664.12818>), conditional distance sampling and time-removal models (Solymos et al. 2013 <doi:10.1111/2041-210X.12106>, Solymos et al. 2018 <doi:10.1650/CONDOR-18-32.1>). Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.
Maintained by Peter Solymos. Last updated 10 months ago.
5 stars 5.27 score 123 scripts
reconverse
i2extras:Functions to Work with 'incidence2' Objects
Provides functions to work with 'incidence2' objects, including a simplified interface for trend fitting and peak estimation. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis (<https://www.reconverse.org/).
Maintained by Tim Taylor. Last updated 8 months ago.
2 stars 5.25 score 22 scripts
tesselle
tabula:Analysis and Visualization of Archaeological Count Data
An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.
Maintained by Nicolas Frerebeau. Last updated 25 days ago.
data-visualizationarchaeologyarchaeological-science
5.10 score 38 scripts 1 dependentstesselle
kairos:Analysis of Chronological Patterns from Archaeological Count Data
A toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.
Maintained by Nicolas Frerebeau. Last updated 25 days ago.
chronologymatrix-seriationarchaeologyarchaeological-science
4.66 score 11 scripts 1 dependents
francescobartolucci
LMest:Generalized Latent Markov Models
Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
Maintained by Francesco Bartolucci. Last updated 3 months ago.
3 stars 4.58 score 42 scripts
wenxuanliu1996
MetabolSSMF:Simplex-Structured Matrix Factorisation for Metabolomics Analysis
Provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) <doi:10.1137/20M1354982>.
Maintained by Wenxuan Liu. Last updated 24 days ago.
4.18 score
wahani
saeRobust:Robust Small Area Estimation
Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.
Maintained by Sebastian Warnholz. Last updated 1 years ago.
1 stars 4.03 score 12 scripts 3 dependents
bbuchsbaum
multivarious:Extensible Data Structures for Multivariate Analysis
Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.
Maintained by Bradley Buchsbaum. Last updated 3 months ago.
3.53 score 17 scripts
labo-lacourse
stepmixr:Interface to 'Python' Package 'StepMix'
This is an interface for the 'Python' package 'StepMix'. It is a 'Python' package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. 'StepMix' handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings.
Maintained by Charles-Édouard Giguère. Last updated 11 months ago.
1 stars 3.48 score 5 scripts
cran
fossil:Palaeoecological and Palaeogeographical Analysis Tools
A set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.
Maintained by Matthew J. Vavrek. Last updated 5 years ago.
1 stars 3.44 score 7 dependents
smac-group
ib:Bias Correction via Iterative Bootstrap
An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.
Maintained by Samuel Orso. Last updated 1 years ago.
2 stars 3.36 score 23 scripts
certara-jcraig
Certara.RsNLME:Pharmacometric Modeling
Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data analysis <https://www.certara.com/app/uploads/2020/06/BR_PhoenixNLME-v4.pdf>. Execution is supported both locally or on remote machines.
Maintained by James Craig. Last updated 4 months ago.
3.01 score 34 scripts 2 dependents
mczek
mcStats:Visualize Results of Statistical Hypothesis Tests
Provides functionality to produce graphs of sampling distributions of test statistics from a variety of common statistical tests. With only a few keystrokes, the user can conduct a hypothesis test and visualize the test statistic and corresponding p-value through the shading of its sampling distribution. Initially created for statistics at Middlebury College.
Maintained by Michael Czekanski. Last updated 5 years ago.
3.00 score 1 scriptsbioc
Rtreemix:Rtreemix: Mutagenetic trees mixture models.
Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc.
Maintained by Jasmina Bogojeska. Last updated 1 months ago.
2.86 score 12 scripts
timhesterberg
resample:Resampling Functions
Bootstrap, permutation tests, and jackknife, featuring easy-to-use syntax.
Maintained by Tim Hesterberg. Last updated 3 years ago.
2.82 score 221 scripts 1 dependents
elilillyco
TSDT:Treatment-Specific Subgroup Detection Tool
Implements a method for identifying subgroups with superior response relative to the overall sample.
Maintained by Brian Denton. Last updated 3 months ago.
2.78 score 60 scripts
henrikbengtsson
aroma.cn:Copy-Number Analysis of Large Microarray Data Sets
Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.
Maintained by Henrik Bengtsson. Last updated 1 years ago.
proprietaryplatformsacghcopynumbervariantssnpmicroarrayonechanneltwochanneldataimportdatarepresentationpreprocessingqualitycontrol
1 stars 2.70 score 9 scriptscran
KGode:Kernel Based Gradient Matching for Parameter Inference in Ordinary Differential Equations
The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <https://proceedings.mlr.press/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.
Maintained by Mu Niu. Last updated 3 years ago.
2.18 score 1 dependents
bouchranasri
MixedIndTests:Tests of Randomness and Tests of Independence
Functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).
Maintained by Bouchra R. Nasri. Last updated 1 years ago.
1.48 score 6 scripts 1 dependents
johnnyzhz
coefficientalpha:Robust Coefficient Alpha and Omega with Missing and Non-Normal Data
Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package 'coefficientalpha' calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Since Version 0.5, the bootstrap confidence intervals were added.
Maintained by Zhiyong Zhang. Last updated 2 years ago.
1.48 score 9 scripts
nussbaummadlene
geoGAM:Select Sparse Geoadditive Models for Spatial Prediction
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.
Maintained by Madlene Nussbaum. Last updated 1 years ago.
1.30 score 20 scripts
