Showing 200 of total 343 results (show query)
melff
mclogit:Multinomial Logit Models, with or without Random Effects or Overdispersion
Provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small.
Maintained by Martin Elff. Last updated 3 months ago.
37.2 match 23 stars 11.03 score 262 scripts 4 dependentsjhelvy
logitr:Logit Models w/Preference & WTP Space Utility Parameterizations
Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.
Maintained by John Helveston. Last updated 5 months ago.
log-likelihoodlogitlogit-modelmixed-logitmlogitmultinomial-regressionmxlmxl-modelspreference-spacepreferenceswillingness-to-paywtp
45.0 match 54 stars 9.10 score 119 scripts 1 dependentsr-forge
mlogit:Multinomial Logit Models
Maximum Likelihood estimation of random utility discrete choice models, as described in Kenneth Train (2009) Discrete Choice Methods with Simulations <doi:10.1017/CBO9780511805271>.
Maintained by Yves Croissant. Last updated 5 years ago.
32.2 match 9.81 score 1.2k scripts 14 dependentsluciu5
antitrust:Tools for Antitrust Practitioners
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Maintained by Charles Taragin. Last updated 6 months ago.
40.8 match 5 stars 5.64 score 36 scripts 2 dependentsschaubert
catdata:Categorical Data
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Maintained by Gunther Schauberger. Last updated 1 years ago.
24.0 match 6.61 score 158 scripts 2 dependentsmlverse
torch:Tensors and Neural Networks with 'GPU' Acceleration
Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
Maintained by Daniel Falbel. Last updated 6 days ago.
8.6 match 520 stars 16.52 score 1.4k scripts 38 dependentsstan-dev
rstanarm:Bayesian Applied Regression Modeling via Stan
Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.
Maintained by Ben Goodrich. Last updated 9 months ago.
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp
8.4 match 393 stars 15.68 score 5.0k scripts 13 dependentsfriendly
nestedLogit:Nested Dichotomy Logistic Regression Models
Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.
Maintained by Michael Friendly. Last updated 10 months ago.
logistic-regressionmultinomial-logistic-regressionpolytomous-variables
20.7 match 10 stars 6.04 score 33 scriptsgdurif
plsgenomics:PLS Analyses for Genomics
Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.
Maintained by Ghislain Durif. Last updated 12 months ago.
17.9 match 5.55 score 140 scripts 2 dependentslaplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 12 months ago.
6.7 match 93 stars 13.45 score 1.8k scripts 60 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.
5.6 match 15.29 score 43k scripts 901 dependentscran
boot:Bootstrap Functions (Originally by Angelo Canty for S)
Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Maintained by Alessandra R. Brazzale. Last updated 7 months ago.
9.8 match 2 stars 8.21 score 2.3k dependentstidymodels
recipes:Preprocessing and Feature Engineering Steps for Modeling
A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.
Maintained by Max Kuhn. Last updated 6 days ago.
4.3 match 584 stars 18.71 score 7.2k scripts 380 dependentssteve-the-bayesian
BoomSpikeSlab:MCMC for Spike and Slab Regression
Spike and slab regression with a variety of residual error distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a few others. Spike and slab regression is Bayesian regression with prior distributions containing a point mass at zero. The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros. Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging. See <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
Maintained by Steven L. Scott. Last updated 1 years ago.
14.2 match 6 stars 5.46 score 95 scripts 5 dependentsrichardli
SUMMER:Small-Area-Estimation Unit/Area Models and Methods for Estimation in R
Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) <doi:10.48550/arXiv.2007.05117>.
Maintained by Zehang R Li. Last updated 2 months ago.
bayesian-inferencesmall-area-estimationspace-time
7.3 match 23 stars 10.28 score 134 scripts 2 dependentsstatdivlab
corncob:Count Regression for Correlated Observations with the Beta-Binomial
Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.
Maintained by Amy D Willis. Last updated 6 months ago.
7.6 match 105 stars 9.64 score 248 scripts 1 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.
6.5 match 40 stars 11.24 score 220 scripts 13 dependentsnicolas-robette
translate.logit:Translation of Logit Regression Coefficients into Percentages
Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.
Maintained by Nicolas Robette. Last updated 2 years ago.
45.1 match 1.60 scorekkholst
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 2 months ago.
latent-variable-modelssimulationstatisticsstructural-equation-models
5.6 match 33 stars 12.85 score 610 scripts 476 dependentsjulianfaraway
faraway:Datasets and Functions for Books by Julian Faraway
Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).
Maintained by Julian Faraway. Last updated 1 months ago.
7.6 match 29 stars 9.43 score 1.7k scripts 1 dependentsbenjaminhlina
ecotox:Analysis of Ecotoxicology
A simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of 'ecotox' comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312).
Maintained by Benjamin L Hlina. Last updated 2 months ago.
biologydose-response-modelinglogitprobittoxicology
15.5 match 3 stars 4.50 score 10 scriptshanase
mlogitBMA:Bayesian Model Averaging for Multinomial Logit Models
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
Maintained by Hana Sevcikova. Last updated 5 months ago.
15.7 match 4.26 score 18 scriptsdewittpe
qwraps2:Quick Wraps 2
A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by 'qwraps2'. 'qwraps2' is a updated version of a package 'qwraps'. The original version 'qwraps' was never submitted to CRAN but can be found at <https://github.com/dewittpe/qwraps/>. The implementation and limited scope of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is fundamentally different from 'qwraps'.
Maintained by Peter DeWitt. Last updated 5 months ago.
6.7 match 37 stars 9.80 score 448 scriptsrstudio
tfprobability:Interface to 'TensorFlow Probability'
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
7.5 match 54 stars 8.63 score 221 scripts 3 dependentsikosmidis
brglm2:Bias Reduction in Generalized Linear Models
Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).
Maintained by Ioannis Kosmidis. Last updated 6 months ago.
adjusted-score-equationsalgorithmsbias-reducing-adjustmentsbias-reductionestimationglmlogistic-regressionnominal-responsesordinal-responsesregressionregression-algorithmsstatistics
6.2 match 32 stars 10.41 score 106 scripts 10 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: Bรผrkner (2017) <doi:10.18637/jss.v080.i01>; Bรผrkner (2018) <doi:10.32614/RJ-2018-017>; Bรผrkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bรผrkner. Last updated 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
3.9 match 1.3k stars 16.61 score 13k scripts 34 dependentsgsucarrat
gets:General-to-Specific (GETS) Modelling and Indicator Saturation Methods
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.
Maintained by Genaro Sucarrat. Last updated 8 months ago.
8.9 match 8 stars 6.89 score 73 scripts 3 dependentsmurrayefford
secr:Spatially Explicit Capture-Recapture
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Maintained by Murray Efford. Last updated 3 days ago.
5.6 match 3 stars 10.18 score 410 scripts 5 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
7.8 match 145 stars 7.09 score 50 scripts 2 dependentsfurrer-lab
abn:Modelling Multivariate Data with Additive Bayesian Networks
The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.
Maintained by Matteo Delucchi. Last updated 5 days ago.
bayesian-networkbinomialcategorical-datagaussiangrouped-datasetsmixed-effectsmultinomialmultivariatepoissonstructure-learninggslopenblascppopenmpjags
7.8 match 6 stars 6.94 score 90 scriptspoissonconsulting
extras:Helper Functions for Bayesian Analyses
Functions to 'numericise' 'R' objects (coerce to numeric objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as 'R' translations of some 'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.
Maintained by Nicole Hill. Last updated 2 months ago.
6.3 match 9 stars 8.49 score 15 scripts 16 dependentslindeloev
mcp:Regression with Multiple Change Points
Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindelรธv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.
Maintained by Jonas Kristoffer Lindelรธv. Last updated 6 months ago.
7.6 match 106 stars 7.03 score 85 scripts 1 dependentsghislainv
hSDM:Hierarchical Bayesian Species Distribution Models
User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Maintained by Ghislain Vieilledent. Last updated 2 years ago.
8.7 match 9 stars 6.04 score 41 scriptsddalthorp
GenEst:Generalized Mortality Estimator
Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Maintained by Daniel Dalthorp. Last updated 2 years ago.
6.7 match 7 stars 7.81 score 55 scripts 2 dependentscran
UPG:Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frรผhwirth-Schnatter & Helga Wagner (2023). Ultimate Pรณlya Gamma Samplers โ Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".
Maintained by Gregor Zens. Last updated 4 months ago.
15.0 match 3.31 score 41 scriptsmbtyers
jagshelper:Extracting and Visualizing Output from 'jagsUI'
Tools are provided to streamline Bayesian analyses in 'JAGS' using the 'jagsUI' package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running 'JAGS' from 'R'. Referenced materials can be found at <DOI:10.1214/ss/1177011136>.
Maintained by Matt Tyers. Last updated 4 months ago.
8.6 match 5.77 score 91 scriptsnovartis
RBesT:R Bayesian Evidence Synthesis Tools
Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19> for details on applying this package while Neuenschwander et al. (2010) <doi:10.1177/1740774509356002> and Schmidli et al. (2014) <doi:10.1111/biom.12242> explain details on the methodology.
Maintained by Sebastian Weber. Last updated 2 months ago.
bayesianclinicalhistorical-datameta-analysiscpp
6.3 match 22 stars 7.87 score 115 scripts 4 dependentsghislainv
jSDM:Joint Species Distribution Models
Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.
Maintained by Ghislain Vieilledent. Last updated 2 years ago.
8.4 match 11 stars 5.87 score 68 scriptsbbolker
margins:Marginal Effects for Model Objects
An R port of the margins command from 'Stata', which can be used to calculate marginal (or partial) effects from model objects.
Maintained by Ben Bolker. Last updated 8 months ago.
5.0 match 2 stars 9.85 score 956 scripts 1 dependentsprojectmosaic
mosaicCore:Common Utilities for Other MOSAIC-Family Packages
Common utilities used in other MOSAIC-family packages are collected here.
Maintained by Randall Pruim. Last updated 1 years ago.
6.7 match 1 stars 7.07 score 113 scripts 26 dependentsdistancedevelopment
mrds:Mark-Recapture Distance Sampling
Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.
Maintained by Laura Marshall. Last updated 2 months ago.
5.8 match 4 stars 8.05 score 78 scripts 7 dependentsbayesiandemography
poputils:Demographic Analysis and Data Manipulation
Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables.
Maintained by John Bryant. Last updated 6 months ago.
8.3 match 5.57 score 49 scripts 1 dependentslin-siting
ForLion:'ForLion' Algorithm to Find D-Optimal Designs for Experiments
Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the 'ForLion' algorithm and 'EW ForLion' algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.
Maintained by Siting Lin. Last updated 1 months ago.
17.0 match 2.70 scoreocbe-uio
contingencytables:Statistical Analysis of Contingency Tables
Provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.
Maintained by Waldir Leoncio. Last updated 7 months ago.
10.7 match 3 stars 4.13 score 8 scripts 1 dependentsprojectmosaic
mosaic:Project MOSAIC Statistics and Mathematics Teaching Utilities
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.
Maintained by Randall Pruim. Last updated 1 years ago.
3.3 match 93 stars 13.32 score 7.2k scripts 7 dependentssnoweye
EMCluster:EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.
Maintained by Wei-Chen Chen. Last updated 6 months ago.
5.8 match 18 stars 7.53 score 123 scripts 2 dependentsnimble-dev
nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Maintained by Christopher Paciorek. Last updated 4 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
3.3 match 169 stars 12.97 score 2.6k scripts 19 dependentsbgctw
logitnorm:Functions for the Logitnormal Distribution
Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
Maintained by Thomas Wutzler. Last updated 1 years ago.
6.6 match 1 stars 6.37 score 89 scripts 13 dependentsgamlss-dev
gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.
Maintained by Mikis Stasinopoulos. Last updated 21 days ago.
4.0 match 4 stars 10.50 score 346 scripts 71 dependentsopenpharma
crmPack:Object-Oriented Implementation of CRM Designs
Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.
Maintained by Daniel Sabanes Bove. Last updated 2 months ago.
5.3 match 21 stars 7.79 score 208 scriptskhvorov45
sclr:Scaled Logistic Regression
Maximum likelihood estimation of the scaled logit model parameters proposed in Dunning (2006) <doi:10.1002/sim.2282>.
Maintained by Arseniy Khvorov. Last updated 5 years ago.
9.4 match 4.36 score 23 scriptsbquast
sigmoid:Sigmoid Functions for Machine Learning
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
Maintained by Bastiaan Quast. Last updated 1 years ago.
6.6 match 2 stars 6.19 score 172 scripts 3 dependentssuyusung
arm:Data Analysis Using Regression and Multilevel/Hierarchical Models
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
Maintained by Yu-Sung Su. Last updated 5 months ago.
3.3 match 25 stars 12.38 score 3.3k scripts 89 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.
3.3 match 29 stars 12.34 score 6.6k scripts 931 dependentstidymodels
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 4 months ago.
1.9 match 1.5k stars 21.56 score 37k scripts 1.4k 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
3.0 match 14 stars 13.47 score 236 scripts 42 dependentscschwarz-stat-sfu-ca
BTSPAS:Bayesian Time-Stratified Population Analysis
Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) <doi:10.1111/j.1541-0420.2011.01599.x>.
Maintained by Carl J Schwarz. Last updated 5 months ago.
6.7 match 1 stars 6.04 score 28 scripts 1 dependentspatriciamar
ShinyItemAnalysis:Test and Item Analysis via Shiny
Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.
Maintained by Patricia Martinkova. Last updated 1 months ago.
assessmentdifferential-item-functioningitem-analysisitem-response-theorypsychometricsshiny
5.1 match 44 stars 7.88 score 105 scripts 3 dependentsdgerbing
lessR:Less Code, More Results
Each function replaces multiple standard R functions. For example, two function calls, Read() and CountAll(), generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for summary statistics via pivot tables, a comprehensive regression analysis, ANOVA and t-test, visualizations including the Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, time series with aggregation and forecasting, color themes, and Trellis (facet) graphics. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, generation and rendering of regression instructions for interpretative output, and interactive visualizations.
Maintained by David W. Gerbing. Last updated 1 months ago.
5.3 match 6 stars 7.47 score 394 scripts 3 dependentshesim-dev
hesim:Health Economic Simulation Modeling and Decision Analysis
A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>, N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106>, and individual-level continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>, encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast.
Maintained by Devin Incerti. Last updated 6 months ago.
health-economic-evaluationmicrosimulationsimulation-modelingcpp
4.9 match 67 stars 8.12 score 41 scriptsglenmartin31
predRupdate:Prediction Model Validation and Updating
Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).
Maintained by Glen P. Martin. Last updated 7 months ago.
7.0 match 7 stars 5.62 score 9 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
3.3 match 115 stars 11.81 score 1.3k scripts 4 dependentsmanuelneumann
MNLpred:Simulated Predicted Probabilities for Multinomial Logit Models
Functions to easily return simulated predicted probabilities and first differences for multinomial logit models. It takes a specified scenario and a multinomial model to predict probabilities with a set of coefficients, drawn from a simulated sampling distribution. The simulated predictions allow for meaningful plots with means and confidence intervals. The methodological approach is based on the principles laid out by King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.
Maintained by Manuel Neumann. Last updated 4 years ago.
7.7 match 12 stars 5.03 score 18 scriptsbayesball
LearnBayes:Learning Bayesian Inference
Contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Maintained by Jim Albert. Last updated 7 years ago.
3.4 match 38 stars 11.34 score 690 scripts 31 dependentsconfig-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
3.3 match 30 stars 11.03 score 97 scripts 34 dependentsjandraor
readsdr:Translate Models from System Dynamics Software into 'R'
The goal of 'readsdr' is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by 'R' libraries. The package accomplishes this goal by parsing 'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.
Maintained by Jair Andrade. Last updated 10 months ago.
5.6 match 19 stars 6.62 score 62 scriptswviechtb
metafor:Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Maintained by Wolfgang Viechtbauer. Last updated 2 days ago.
meta-analysismixed-effectsmultilevel-modelsmultivariate
2.3 match 246 stars 16.30 score 4.9k scripts 92 dependentszpneal
backbone:Extracts the Backbone from Graphs
An implementation of methods for extracting an unweighted unipartite graph (i.e. a backbone) from an unweighted unipartite graph, a weighted unipartite graph, the projection of an unweighted bipartite graph, or the projection of a weighted bipartite graph (Neal, 2022 <doi:10.1371/journal.pone.0269137>).
Maintained by Zachary Neal. Last updated 1 years ago.
5.2 match 41 stars 7.06 score 31 scripts 2 dependentsmarkbravington
mvbutils:General utilities, workspace organization, code and docu editing, live package maintenance, etc
Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.
Maintained by Mark V. Bravington. Last updated 6 days ago.
5.6 match 6.53 score 138 scripts 18 dependentsrje42
rje:Miscellaneous Useful Functions for Statistics
A series of functions in some way considered useful to the author. These include methods for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations, fast transformations, and more...
Maintained by Robin Evans. Last updated 12 months ago.
5.6 match 6.50 score 173 scripts 10 dependentsmarc-girondot
HelpersMG:Tools for Environmental Analyses, Ecotoxicology and Various R Functions
Contains miscellaneous functions useful for managing 'NetCDF' files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, tide level, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Maintained by Marc Girondot. Last updated 2 months ago.
7.6 match 4 stars 4.59 score 160 scripts 4 dependentsvincentporretta
VWPre:Tools for Preprocessing Visual World Data
Gaze data from the Visual World Paradigm requires significant preprocessing prior to plotting and analyzing the data. This package provides functions for preparing visual world eye-tracking data for statistical analysis and plotting. It can prepare data for linear analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM). Additionally, it contains various plotting functions for creating grand average and conditional average plots. See the vignette for samples of the functionality. Currently, the functions in this package are designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. While we would like to add functionality for data collected with other systems in the future, the current package is considered to be feature-complete; further updates will mainly entail maintenance and the addition of minor functionality.
Maintained by Vincent Porretta. Last updated 4 years ago.
7.9 match 4.28 score 80 scripts 1 dependentsknudson1
glmm:Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Maintained by Christina Knudson. Last updated 6 months ago.
7.3 match 2 stars 4.64 score 216 scriptszhenkewu
baker:"Nested Partially Latent Class Models"
Provides functions to specify, fit and visualize nested partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>) for inference of population disease etiology and individual diagnosis. In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package.
Maintained by Zhenke Wu. Last updated 11 months ago.
bayesiancase-controllatent-class-analysisjagscpp
5.6 match 8 stars 6.00 score 21 scriptsmauricio1986
gmnl:Multinomial Logit Models with Random Parameters
An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.
Maintained by Mauricio Sarrias. Last updated 3 years ago.
7.5 match 4 stars 4.27 score 51 scriptsgiabaio
bmhe:This Package Creates a Set of Functions Useful for Bayesian modelling
A set of utility functions that can be used to post-process BUGS or JAGS objects as well as other to facilitate various Bayesian modelling activities (including in HTA).
Maintained by Gianluca Baio. Last updated 11 days ago.
bayesian-statisticsbugscost-effectiveness-analysisjagstidyverse
10.6 match 2 stars 3.00 score 7 scriptsbristol-vaccine-centre
testerror:Uncertainty in Multiplex Panel Testing
Provides methods to support the estimation of epidemiological parameters based on the results of multiplex panel tests.
Maintained by Robert Challen. Last updated 12 months ago.
9.3 match 1 stars 3.40 score 4 scriptsr-forge
survey:Analysis of Complex Survey Samples
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs.
Maintained by "Thomas Lumley". Last updated 6 months ago.
2.3 match 1 stars 13.94 score 13k scripts 232 dependentscschwarz-stat-sfu-ca
Petersen:Estimators for Two-Sample Capture-Recapture Studies
A comprehensive implementation of Petersen-type estimators and its many variants for two-sample capture-recapture studies. A conditional likelihood approach is used that allows for tag loss; non reporting of tags; reward tags; categorical, geographical and temporal stratification; partial stratification; reverse capture-recapture; and continuous variables in modeling the probability of capture. Many examples from fisheries management are presented.
Maintained by Carl Schwarz. Last updated 20 days ago.
6.7 match 1 stars 4.48 score 12 scriptsphilipdelff
NMcalc:Basic Calculations for PK/PD Modeling
Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.
Maintained by Philip Delff. Last updated 5 months ago.
7.6 match 3 stars 3.95 score 5 scripts 1 dependentsnyilin
ROlogit:Fit Rank-Ordered Logit (RO-Logit) Model
Implements the rank-ordered logit (RO-logit) model for stratified analysis of continuous outcomes introduced by Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on the heuristic residuals and estimates in linear scales are available from the package, and outcomes with ties are supported.
Maintained by Ning Yilin. Last updated 4 years ago.
10.9 match 2.70 score 2 scriptslbelzile
BMAmevt:Multivariate Extremes: Bayesian Estimation of the Spectral Measure
Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.
Maintained by Leo Belzile. Last updated 2 years ago.
7.6 match 3.90 score 16 scriptscran
flexmix:Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
Maintained by Bettina Gruen. Last updated 17 days ago.
3.6 match 5 stars 8.19 score 113 dependentscanmod
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
3.3 match 4 stars 8.89 score 246 scripts 1 dependentssmartdata-analysis-and-statistics
metamisc:Meta-Analysis of Diagnosis and Prognosis Research Studies
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Maintained by Thomas Debray. Last updated 1 months ago.
meta-analysisprognosisprognostic-models
3.9 match 7 stars 7.48 score 102 scriptssdorairaj
binom:Binomial Confidence Intervals for Several Parameterizations
Constructs confidence intervals on the probability of success in a binomial experiment via several parameterizations.
Maintained by Sundar Dorai-Raj. Last updated 3 years ago.
4.0 match 7.10 score 1.1k scripts 37 dependentsarives
rr2:R2s for Regression Models
Three methods to calculate R2 for models with correlated errors, including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in Ives 2018 <doi:10.1093/sysbio/syy060>.
Maintained by Anthony Ives. Last updated 2 years ago.
4.3 match 18 stars 6.60 score 104 scripts 1 dependentsnlmixr2
nlmixr2:Nonlinear Mixed Effects Models in Population PK/PD
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Maintained by Matthew Fidler. Last updated 25 days ago.
3.3 match 50 stars 8.45 score 120 scripts 3 dependentsrsparapa
BART:Bayesian Additive Regression Trees
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
Maintained by Rodney Sparapani. Last updated 9 months ago.
3.4 match 14 stars 7.96 score 474 scripts 10 dependentsfalkcarl
pln:Polytomous Logit-Normit (Graded Logistic) Model Estimation
Performs bivariate composite likelihood and full information maximum likelihood estimation for polytomous logit-normit (graded logistic) item response theory (IRT) models.
Maintained by Carl F. Falk. Last updated 5 months ago.
10.1 match 2.70 score 3 scriptsnlmixr2
nlmixr2est:Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Maintained by Matthew Fidler. Last updated 26 days ago.
3.3 match 9 stars 8.26 score 26 scripts 9 dependentsepiforecasts
epinowcast:Flexible Hierarchical Nowcasting
Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.
Maintained by Sam Abbott. Last updated 11 months ago.
cmdstanreffective-reproduction-number-estimationepidemiologyinfectious-disease-surveillancenowcastingoutbreak-analysispandemic-preparednessreal-time-infectious-disease-modellingstan
3.4 match 61 stars 7.88 score 65 scriptsmechantrouquin
landsepi:Landscape Epidemiology and Evolution
A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removedโ) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaรฏx, Jean-Franรงois Rey, Luke G Barrett, Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
Maintained by Jean-Franรงois Rey. Last updated 6 months ago.
7.6 match 3.58 score 18 scriptsanestistouloumis
SimCorMultRes:Simulates Correlated Multinomial Responses
Simulates correlated multinomial responses conditional on a marginal model specification.
Maintained by Anestis Touloumis. Last updated 12 months ago.
binarylongitudinal-studiesmultinomialsimulation
4.5 match 7 stars 6.04 score 26 scripts 2 dependentsimt:Impact Measurement Toolkit
A toolkit for causal inference in experimental and observational studies. Implements various simple Bayesian models including linear, negative binomial, and logistic regression for impact estimation. Provides functionality for randomization and checking baseline equivalence in experimental designs. The package aims to simplify the process of impact measurement for researchers and analysts across different fields. Examples and detailed usage instructions are available at <https://book.martinez.fyi>.
Maintained by Ignacio Martinez. Last updated 5 months ago.
6.9 match 3 stars 3.88 score 6 scriptsepinowcast
epinowcast:Flexible Hierarchical Nowcasting
Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.
Maintained by Sam Abbott. Last updated 11 months ago.
cmdstanreffective-reproduction-number-estimationepidemiologyinfectious-disease-surveillancenowcastingoutbreak-analysispandemic-preparednessreal-time-infectious-disease-modellingstan
3.4 match 61 stars 7.79 score 71 scriptsandland
logisticPCA:Binary Dimensionality Reduction
Dimensionality reduction techniques for binary data including logistic PCA.
Maintained by Andrew J. Landgraf. Last updated 5 years ago.
4.0 match 50 stars 6.54 score 69 scriptsyouyifong
kyotil:Utility Functions for Statistical Analysis Report Generation and Monte Carlo Studies
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Maintained by Youyi Fong. Last updated 8 days ago.
3.3 match 7.87 score 236 scripts 7 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.
5.4 match 2 stars 4.84 score 23 scriptsjoemolloy
mixl:Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets
Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
Maintained by Joseph Molloy. Last updated 1 years ago.
5.4 match 4 stars 4.79 score 8 scriptsbioc
minfi:Analyze Illumina Infinium DNA methylation arrays
Tools to analyze & visualize Illumina Infinium methylation arrays.
Maintained by Kasper Daniel Hansen. Last updated 4 months ago.
immunooncologydnamethylationdifferentialmethylationepigeneticsmicroarraymethylationarraymultichanneltwochanneldataimportnormalizationpreprocessingqualitycontrol
2.0 match 60 stars 12.83 score 996 scripts 26 dependentstheomichelot
hmmTMB:Fit Hidden Markov Models using Template Model Builder
Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2022) <arXiv:2211.14139>.
Maintained by Theo Michelot. Last updated 1 months ago.
3.9 match 53 stars 6.57 score 64 scriptssnoweye
MixfMRI:Mixture fMRI Clustering Analysis
Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) <doi:10.1002/hbm.26425>) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques.
Maintained by Wei-Chen Chen. Last updated 5 months ago.
5.9 match 2 stars 4.26 score 18 scriptsvandomed
dvmisc:Convenience Functions, Moving Window Statistics, and Graphics
Collection of functions for running and summarizing statistical simulation studies, creating visualizations (e.g. CART Shiny app, histograms with fitted probability mass/density functions), calculating moving-window statistics efficiently, and performing common computations.
Maintained by Dane R. Van Domelen. Last updated 4 years ago.
aicbmihistogramsmiscellaneouscpp
4.0 match 1 stars 6.18 score 125 scripts 8 dependentsarnoxieseg
LLM:Logit Leaf Model Classifier for Binary Classification
Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Maintained by Arno De Caigny. Last updated 5 years ago.
10.5 match 2 stars 2.30 score 5 scriptsecpolley
SuperLearner:Super Learner Prediction
Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Maintained by Eric Polley. Last updated 1 years ago.
1.9 match 274 stars 12.85 score 2.1k scripts 36 dependentsboehringer-ingelheim
BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies
Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.
Maintained by Christian Stock. Last updated 11 months ago.
bayesian-methodscausal-inferenceclinical-trialestimandmcmc-methodspharmaceutical-developmentprincipal-stratificationsimulationstantime-to-eventcpp
7.6 match 3.18 scorejsocolar
flocker:Flexible Occupancy Estimation with Stan
Fit occupancy models in 'Stan' via 'brms'. The full variety of 'brms' formula-based effects structures are available to use in multiple classes of occupancy model, including single-season models, models with data augmentation for never-observed species, dynamic (multiseason) models with explicit colonization and extinction processes, and dynamic models with autologistic occupancy dynamics. Formulas can be specified for all relevant distributional terms, including detection and one or more of occupancy, colonization, extinction, and autologistic depending on the model type. Several important forms of model post-processing are provided. References: Bรผrkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Socolar & Mills (2023) <doi:10.1101/2023.10.26.564080>.
Maintained by Jacob B. Socolar. Last updated 2 months ago.
3.5 match 30 stars 6.78 score 20 scriptsmyko101
rando:Context Aware Random Numbers
Provides random number generating functions that are much more context aware than the built-in functions. The functions are also much safer, as they check for incompatible values, and more reproducible.
Maintained by Michael Barrowman. Last updated 4 years ago.
6.7 match 7 stars 3.54 score 6 scriptsjenswahl
stochvolTMB:Likelihood Estimation of Stochastic Volatility Models
Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).
Maintained by Jens Wahl. Last updated 1 months ago.
5.1 match 8 stars 4.60 scorecran
bayesdistreg:Bayesian Distribution Regression
Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.
Maintained by Emmanuel Tsyawo. Last updated 6 years ago.
11.9 match 1.95 score 18 scriptsrvlenth
emmeans:Estimated Marginal Means, aka Least-Squares Means
Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Maintained by Russell V. Lenth. Last updated 3 days ago.
1.2 match 377 stars 19.19 score 13k scripts 187 dependentsmjskay
ggdist:Visualizations of Distributions and Uncertainty
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.
Maintained by Matthew Kay. Last updated 4 months ago.
ggplot2uncertaintyuncertainty-visualizationvisualizationcpp
1.5 match 856 stars 15.24 score 3.1k scripts 61 dependentsspluque
diveMove:Dive Analysis and Calibration
Utilities to represent, visualize, filter, analyse, and summarize time-depth recorder (TDR) data. Miscellaneous functions for handling location data are also provided.
Maintained by Sebastian P. Luque. Last updated 5 months ago.
animal-behaviorbehavioural-ecologybiologydivingscience
3.3 match 6 stars 6.75 score 55 scriptsdnzmarcio
ewoc:Escalation with Overdose Control
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.
Maintained by Marcio A. Diniz. Last updated 3 years ago.
6.6 match 2 stars 3.30 score 20 scriptsjacob-long
jtools:Analysis and Presentation of Social Scientific Data
This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Support for models produced by the survey and lme4 packages are points of emphasis.
Maintained by Jacob A. Long. Last updated 6 months ago.
1.5 match 167 stars 14.48 score 4.0k scripts 14 dependentscdriveraus
ctsem:Continuous Time Structural Equation Modelling
Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.
Maintained by Charles Driver. Last updated 12 days ago.
stochastic-differential-equationstime-seriescpp
2.3 match 42 stars 9.58 score 366 scripts 1 dependentsjmm34
bayess:Bayesian Essentials with R
Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.
Maintained by Jean-Michel Marin. Last updated 1 years ago.
5.4 match 3 stars 4.01 score 68 scriptsbioc
tidybulk:Brings transcriptomics to the tidyverse
This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion.
Maintained by Stefano Mangiola. Last updated 5 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsbioconductorbulk-transcriptional-analysesdeseq2differential-expressionedgerensembl-idsentrezgene-symbolsgseamds-dimensionspcapiperedundancytibbletidytidy-datatidyversetranscriptstsne
2.3 match 168 stars 9.48 score 172 scripts 1 dependentsa-fernihough
mfx:Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
Maintained by Alan Fernihough. Last updated 6 years ago.
4.3 match 4.97 score 386 scriptsnlmixr2
nonmem2rx:Converts 'NONMEM' Models to 'rxode2'
'NONMEM' has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 <doi:10.1002/psp4.12404>). This tool allows you to convert 'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) and with simple models 'nlmixr2' syntax (Fidler et al (2019) <doi:10.1002/psp4.12445>). The 'nlmixr2' syntax requires the residual specification to be included and it is not always translated. If available, the 'rxode2' model will read in the 'NONMEM' data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit.
Maintained by Matthew Fidler. Last updated 4 months ago.
nlmixr2nonmempharmacometricsrxode2cpp
3.3 match 11 stars 6.42 score 23 scripts 1 dependentsrmheiberger
HH:Statistical Analysis and Data Display: Heiberger and Holland
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Maintained by Richard M. Heiberger. Last updated 1 months ago.
3.3 match 3 stars 6.42 score 752 scripts 5 dependentsnlmixr2
nlmixr2lib:A Model Library for 'nlmixr2'
A model library for 'nlmixr2'. The models include (and plan to include) pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. Where applicable, references for each model are included in the meta-data for each individual model. The package also includes model composition and modification functions to make model updates easier.
Maintained by Bill Denney. Last updated 2 months ago.
3.3 match 6 stars 6.38 score 9 scriptsweberse2
OncoBayes2:Bayesian Logistic Regression for Oncology Dose-Escalation Trials
Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Maintained by Sebastian Weber. Last updated 15 days ago.
9.7 match 2.18 score 15 scriptsjamesliley
OptHoldoutSize:Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score
Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <arXiv:2202.06374> for details of methods.
Maintained by James Liley. Last updated 3 years ago.
6.6 match 3.18 score 10 scriptsghurault
HuraultMisc:Guillem Hurault Functions' Library
Contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.
Maintained by Guillem Hurault. Last updated 3 months ago.
bayesian-statisticsdata-analysisstatistical-models
7.0 match 2.95 score 18 scriptstaddylab
maptpx:MAP Estimation of Topic Models
Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) 'On estimation and selection for topic models'. Previous versions of this code were included as part of the 'textir' package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Maintained by Matt Taddy. Last updated 5 years ago.
map-estimationtopic-modelingopenblas
3.3 match 19 stars 6.27 score 326 scripts 2 dependentsffqueiroz
PLreg:Power Logit Regression for Modeling Bounded Data
Power logit regression models for bounded continuous data, in which the density generator may be normal, Student-t, power exponential, slash, hyperbolic, sinh-normal, or type II logistic. Diagnostic tools associated with the fitted model, such as the residuals, local influence measures, leverage measures, and goodness-of-fit statistics, are implemented. The estimation process follows the maximum likelihood approach and, currently, the package supports two types of estimators: the usual maximum likelihood estimator and the penalized maximum likelihood estimator. More details about power logit regression models are described in Queiroz and Ferrari (2022) <arXiv:2202.01697>.
Maintained by Felipe Queiroz. Last updated 2 years ago.
7.4 match 2.70 score 2 scriptschgigot
epiphy:Analysis of Plant Disease Epidemics
A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) <doi:10.1094/9780890545058> for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package.
Maintained by Christophe Gigot. Last updated 1 years ago.
3.3 match 15 stars 6.05 score 37 scriptsjmhewitt
bisque:Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models
Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details. 'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models. The resulting approximations are computationally efficient for many hierarchical Bayesian models. The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Maintained by Joshua Hewitt. Last updated 5 years ago.
6.3 match 1 stars 3.18 score 10 scriptsmlverse
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.
2.0 match 89 stars 9.86 score 318 scripts 4 dependentswinvector
vtreat:A Statistically Sound 'data.frame' Processor/Conditioner
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 'vtreat' prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.
Maintained by John Mount. Last updated 2 months ago.
categorical-variablesmachine-learning-algorithmsnested-modelsprepare-data
1.8 match 285 stars 11.19 score 328 scripts 1 dependentsgogonzo
sport:Sequential Pairwise Online Rating Techniques
Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refers to Mark E. Glickman (1999) <http://www.glicko.net/research/glicko.pdf>; Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; Ruby C. Weng, Chih-Jen Lin (2011) <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>; W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
Maintained by Dawid Kaลฤdkowski. Last updated 5 years ago.
3.3 match 25 stars 5.78 score 24 scriptslimengbinggz
ddtlcm:Latent Class Analysis with Dirichlet Diffusion Tree Process Prior
Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. Reference: Li et al. (2023) <arXiv:2306.04700>.
Maintained by Mengbing Li. Last updated 8 months ago.
3.3 match 6 stars 5.80 score 8 scriptsbioc
zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data
Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.
Maintained by Davide Risso. Last updated 5 months ago.
immunooncologydimensionreductiongeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell
1.8 match 43 stars 10.53 score 190 scripts 6 dependentsfriendly
vcdExtra:'vcd' Extensions and Additions
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.
Maintained by Michael Friendly. Last updated 5 months ago.
categorical-data-visualizationgeneralized-linear-modelsmosaic-plots
1.8 match 24 stars 10.34 score 472 scripts 3 dependentsai4ci
ggoutbreak:Estimate Incidence, Proportions and Exponential Growth Rates
Simple statistical models and visualisations for calculating the incidence, proportion, exponential growth rate, and reproduction number of infectious disease case time series. This toolkit was largely developed during the COVID-19 pandemic.
Maintained by Robert Challen. Last updated 1 months ago.
4.3 match 1 stars 4.30 scorearcher-yang-lab
gglasso:Group Lasso Penalized Learning Using a Unified BMD Algorithm
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
Maintained by Yi Yang. Last updated 5 years ago.
2.3 match 10 stars 8.12 score 292 scripts 10 dependentsdmphillippo
multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data
Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.
Maintained by David M. Phillippo. Last updated 3 days ago.
2.0 match 35 stars 9.11 score 163 scriptsstaffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 3 months ago.
3.3 match 5.42 score 4.4k scriptsjamesliley
SPARRAfairness:Analysis of Differential Behaviour of SPARRA Score Across Demographic Groups
The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset.
Maintained by James Liley. Last updated 4 months ago.
6.6 match 2.70 score 4 scriptsamerican-institutes-for-research
EdSurvey:Analysis of NCES Education Survey and Assessment Data
Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/en/about/directorates/directorate-for-education-and-skills.html>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).
Maintained by Paul Bailey. Last updated 16 days ago.
2.3 match 10 stars 7.86 score 139 scripts 1 dependentsr-forge
SpatialExtremes:Modelling Spatial Extremes
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Maintained by Mathieu Ribatet. Last updated 11 months ago.
3.3 match 5.36 score 189 scripts 2 dependentserhard-lab
lfc:Log Fold Change Distribution Tools for Working with Ratios of Counts
Ratios of count data such as obtained from RNA-seq are modelled using Bayesian statistics to derive posteriors for effects sizes. This approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696> and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.
Maintained by Florian Erhard. Last updated 2 years ago.
bayesiantranscriptomicsdifferentialexpression
3.3 match 7 stars 5.32 score 5 scripts 2 dependentsdatalorax
equatiomatic:Transform Models into 'LaTeX' Equations
The goal of 'equatiomatic' is to reduce the pain associated with writing 'LaTeX' formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model.
Maintained by Philippe Grosjean. Last updated 7 days ago.
1.5 match 619 stars 11.75 score 424 scripts 5 dependentscran
givitiR:The GiViTI Calibration Test and Belt
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
Maintained by Giovanni Nattino. Last updated 8 years ago.
5.3 match 3.32 score 21 scriptskgoldfeld
simstudy:Simulation of Study Data
Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).
Maintained by Keith Goldfeld. Last updated 8 months ago.
data-generationdata-simulationsimulationstatistical-modelscpp
1.6 match 82 stars 11.00 score 972 scripts 1 dependentspik-piam
edgeTransport:Prepare EDGE Transport Data for the REMIND model
EDGE-T is a fork of the GCAM transport module https://jgcri.github.io/gcam-doc/energy.html#transportation with a high level of detail in its representation of technological and modal options. It is a partial equilibrium model with a nested multinomial logit structure and relies on the modified logit formulation. Most of the sources are not publicly available. PIK-internal users can find the sources in the distributed file system in the folder `/p/projects/rd3mod/inputdata/sources/EDGE-Transport-Standalone`.
Maintained by Johanna Hoppe. Last updated 3 days ago.
2.5 match 5 stars 6.84 score 16 scripts 2 dependentsjhstaudacher
EvolutionaryGames:Important Concepts of Evolutionary Game Theory
Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided.
Maintained by Jochen Staudacher. Last updated 3 years ago.
5.6 match 2 stars 3.11 score 32 scriptsterminological
ggrrr:Addressing Annoyances and Irritations
Visualisation hacks, tabular data helpers, fonts, caching, tidy data functions. It is an swiss army knife, jack of all trades.
Maintained by Robert Challen. Last updated 9 months ago.
6.3 match 1 stars 2.74 score 11 scriptsstatmanrobin
Stat2Data:Datasets for Stat2
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). The package also includes data for the first edition, Stat2: Building Models for a World of Data and a few functions for plotting diagnostics.
Maintained by Robin Lock. Last updated 6 years ago.
3.4 match 5 stars 4.94 score 544 scriptssachsmc
pseval:Methods for Evaluating Principal Surrogates of Treatment Response
Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.
Maintained by Michael C Sachs. Last updated 6 years ago.
4.3 match 1 stars 3.88 score 15 scriptsbioc
gpls:Classification using generalized partial least squares
Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
classificationmicroarrayregression
4.0 match 4.04 score 11 scriptspoissonconsulting
bbousims:Simulate Boreal Caribou Survival, Recruitment and Population Growth
Simulates survival and recruitment data for boreal caribou populations using hierarchical Bayesian models.
Maintained by Seb Dalgarno. Last updated 4 months ago.
3.3 match 4.78 score 6 scripts 1 dependentskevinmcgregor
countprop:Calculate Model-Based Metrics of Proportionality on Count-Based Compositional Data
Calculates metrics of proportionality using the logit-normal multinomial model. It can also provide empirical and plugin estimates of these metrics.
Maintained by Kevin McGregor. Last updated 2 years ago.
4.2 match 3.70 score 3 scriptsmelff
memisc:Management of Survey Data and Presentation of Analysis Results
An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.
Maintained by Martin Elff. Last updated 11 days ago.
1.3 match 46 stars 12.34 score 1.2k scripts 13 dependentsjongheepark
MCMCpack:Markov Chain Monte Carlo (MCMC) Package
Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.
Maintained by Jong Hee Park. Last updated 7 months ago.
1.6 match 13 stars 9.40 score 2.6k scripts 150 dependentsgenentech
psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation
Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.
Maintained by Matt Secrest. Last updated 1 months ago.
bayesian-dynamic-borrowingpsborrow2simulation-study
1.9 match 18 stars 7.87 score 16 scriptsnlmixr2
monolix2rx:Converts 'Monolix' Models to 'rxode2'
'Monolix' is a tool for running mixed effects model using 'saem'. This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When 'lixoftConnectors' is available, 'Monolix' can be used to load its model library instead manually setting up text files (which only works with old versions of 'Monolix').
Maintained by Matthew Fidler. Last updated 4 months ago.
monolixnlmixr2pharmacometricsrxode2cpp
3.3 match 1 stars 4.47 score 14 scripts 1 dependentsbrockk
trialr:Clinical Trial Designs in 'rstan'
A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.
Maintained by Kristian Brock. Last updated 1 years ago.
1.7 match 41 stars 8.55 score 106 scripts 3 dependentsjonasmoss
univariateML:Maximum Likelihood Estimation for Univariate Densities
User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.
Maintained by Jonas Moss. Last updated 14 days ago.
densityestimationmaximum-likelihood
1.8 match 8 stars 8.10 score 62 scripts 7 dependentsdeweyme
metap:Meta-Analysis of Significance Values
The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and routines for graphical display.
Maintained by Michael Dewey. Last updated 1 days ago.
1.8 match 8.08 score 642 scripts 14 dependentsvladimirholy
gasmodel:Generalized Autoregressive Score Models
Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>. Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method.
Maintained by Vladimรญr Holรฝ. Last updated 1 years ago.
2.6 match 14 stars 5.45 score 2 scriptsekstroem
isdals:Datasets for Introduction to Statistical Data Analysis for the Life Sciences
Provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrรธm and Sรธrensen (2014).
Maintained by Claus Ekstrom. Last updated 2 years ago.
5.6 match 2.51 score 108 scripts 1 dependentsdrjp
nimbleNoBounds:Transformed Distributions for Improved MCMC Efficiency
A collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.
Maintained by David Pleydell. Last updated 9 months ago.
3.8 match 1 stars 3.70 score 2 scriptsnliulab
AutoScore:An Interpretable Machine Learning-Based Automatic Clinical Score Generator
A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
Maintained by Feng Xie. Last updated 15 days ago.
1.8 match 32 stars 7.70 score 30 scriptscran
oglmx:Estimation of Ordered Generalized Linear Models
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.
Maintained by Nathan Carroll. Last updated 7 years ago.
6.9 match 1 stars 2.00 scoreatlas-aai
dcm2:Calculating the M2 Model Fit Statistic for Diagnostic Classification Models
A collection of functions for calculating the M2 model fit statistic for diagnostic classification models as described by Liu et al. (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic.
Maintained by Jeffrey Hoover. Last updated 11 months ago.
3.3 match 4.18 score 3 scripts 1 dependentsmarco-geraci
Qtools:Utilities for Quantiles
Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Maintained by Marco Geraci. Last updated 1 years ago.
3.3 match 4.10 score 33 scripts 2 dependentsnicolas-robette
GDAtools:Geometric Data Analysis
Many tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>), such as MCA variants (Specific Multiple Correspondence Analysis, Class Specific Analysis), many graphical and statistical aids to interpretation (structuring factors, concentration ellipses, inductive tests, bootstrap validation, etc.) and multiple-table analysis (Multiple Factor Analysis, between- and inter-class analysis, Principal Component Analysis and Correspondence Analysis with Instrumental Variables, etc.).
Maintained by Nicolas Robette. Last updated 10 months ago.
2.3 match 10 stars 5.93 score 94 scripts 2 dependentsngreifer
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
1.1 match 112 stars 11.58 score 508 scripts 3 dependentsmezarafael
Bhat:General Likelihood Exploration
Provides functions for Maximum Likelihood Estimation, Markov Chain Monte Carlo, finding confidence intervals. The implementation is heavily based on the original Fortran source code translated to R.
Maintained by Rafael Meza. Last updated 3 years ago.
6.1 match 2.16 score 36 scriptsesm-ispm-unibe-ch
predieval:Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
Maintained by Orestis Efthimiou. Last updated 2 years ago.
6.6 match 2.00 score 1 scriptsjosie-athens
pubh:A Toolbox for Public Health and Epidemiology
A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand 'ggplot2' plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
Maintained by Josie Athens. Last updated 5 months ago.
2.3 match 5 stars 5.73 score 72 scriptsrstudio
tfestimators:Interface to 'TensorFlow' Estimators
Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/guide/estimator>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.
Maintained by Tomasz Kalinowski. Last updated 3 years ago.
1.5 match 57 stars 8.42 score 170 scriptssinafala
SVDMx:Child/Child-Adult Mortality-Indexed Model Mortality Age Schedules
Model age schedules of mortality, nqx, suitable for a life table. This package implements the SVD-Comp mortality model indexed by either child or child/adult mortality. Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.
Maintained by Samuel Clark. Last updated 1 months ago.
7.4 match 1.70 scoreflr
FLSRTMB:FLSR in TMB
Estimates FLR spawner recruitment relationships in TMB
Maintained by Henning Winker. Last updated 15 days ago.
stock-recruitfisheriesflrtmbadcpp
3.4 match 3.67 score 26 scripts 1 dependentsjohnaponte
afdx:Diagnosis Performance Using Attributable Fraction
Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.
Maintained by John J. Aponte. Last updated 4 years ago.
3.1 match 1 stars 4.00 score 7 scriptscran
InterSIM:Simulation of Inter-Related Genomic Datasets
Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.
Maintained by Prabhakar Chalise. Last updated 2 months ago.
5.6 match 2.26 score 2 dependentschoi-phd
TestDesign:Optimal Test Design Approach to Fixed and Adaptive Test Construction
Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests. Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony', 'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.
Maintained by Seung W. Choi. Last updated 6 months ago.
1.7 match 3 stars 7.34 score 37 scripts 2 dependentsstephenrho
pmcalibration:Calibration Curves for Clinical Prediction Models
Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 'pmcalibration' allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; Austin et al. (2020) <doi:10.1002/sim.8570>.
Maintained by Stephen Rhodes. Last updated 23 days ago.
3.3 match 3.73 score 54 scriptsyikeshu0611
cutoff:Seek the Significant Cutoff Value
Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Missing values will be deleted by na.omit() function before analysis.
Maintained by Jing Zhang. Last updated 5 years ago.
3.3 match 1 stars 3.67 score 31 scripts 1 dependentsjulia-wrobel
registr:Curve Registration for Exponential Family Functional Data
A method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963> and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology.
Maintained by Julia Wrobel. Last updated 3 years ago.
1.9 match 16 stars 6.27 score 29 scriptspdwaggoner
purging:Simple Method for Purging Mediation Effects among Independent Variables
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
Maintained by Philip D. Waggoner. Last updated 6 years ago.
3.9 match 2 stars 3.00 score 5 scriptsadamlilith
statisfactory:Statistical and Geometrical Tools
A collection of statistical and geometrical tools including the aligned rank transform (ART; Higgins et al. 1990 <doi:10.4148/2475-7772.1443>; Peterson 2002 <doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011 <doi:10.1145/1978942.1978963>), 2-D histograms and histograms with overlapping bins, a function for making all possible formulae within a set of constraints, amongst others.
Maintained by Adam B. Smith. Last updated 5 months ago.
2d-histogramsaligned-rank-transformsampling
3.4 match 3.38 score 16 scripts 1 dependentsbioc
biscuiteer:Convenience Functions for Biscuit
A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc.
Maintained by Jacob Morrison. Last updated 5 months ago.
dataimportmethylseqdnamethylation
1.9 match 6 stars 6.16 score 16 scripts