R-universe search: logit

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 dependents

jhelvy

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-likelihood logit logit-model mixed-logit mlogit multinomial-regression mxl mxl-models preference-space preferences willingness-to-pay wtp

45.0 match 54 stars 9.10 score 119 scripts 1 dependents

r-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 dependents

luciu5

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 dependents

cran

bayesm:Bayesian Inference for Marketing/Micro-Econometrics

Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).

Maintained by Peter Rossi. Last updated 1 years ago.

openblas cpp

22.6 match 20 stars 8.20 score 322 scripts 43 dependents

schaubert

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 dependents

mlverse

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.

autograd deep-learning torch cpp

8.6 match 520 stars 16.52 score 1.4k scripts 38 dependents

r-gregmisc

gtools:Various R Programming Tools

Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords').

Maintained by Ben Bolker. Last updated 9 months ago.

9.7 match 25 stars 14.47 score 11k scripts 1.1k dependents

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 1 months ago.

fortran

12.9 match 10 stars 10.67 score 3.6k scripts 169 dependents

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

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

Maintained by Ben Goodrich. Last updated 9 months ago.

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

8.4 match 393 stars 15.68 score 5.0k scripts 13 dependents

friendly

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-regression multinomial-logistic-regression polytomous-variables

20.7 match 10 stars 6.04 score 33 scripts

andrisignorell

DescTools:Tools for Descriptive Statistics

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

Maintained by Andri Signorell. Last updated 9 hours ago.

fortran cpp

6.5 match 87 stars 16.70 score 7.7k scripts 99 dependents

gdurif

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 dependents

laplacesdemonr

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 dependents

r-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 dependents

cran

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 dependents

tidymodels

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 dependents

steve-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.

cpp

14.2 match 6 stars 5.46 score 95 scripts 5 dependents

richardli

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-inference small-area-estimation space-time

7.3 match 23 stars 10.28 score 134 scripts 2 dependents

statdivlab

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

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

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

7.6 match 105 stars 9.64 score 248 scripts 1 dependents

nlmixr2

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.

fortran openblas cpp openmp

6.5 match 40 stars 11.24 score 220 scripts 13 dependents

nicolas-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 score

kkholst

lava:Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

Maintained by Klaus K. Holst. Last updated 2 months ago.

latent-variable-models simulation statistics structural-equation-models

5.6 match 33 stars 12.85 score 610 scripts 476 dependents

julianfaraway

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.

data

7.6 match 29 stars 9.43 score 1.7k scripts 1 dependents

benjaminhlina

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.

biology dose-response-modeling logit probit toxicology

15.5 match 3 stars 4.50 score 10 scripts

stephanehess

apollo:Tools for Choice Model Estimation and Application

Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <http://www.ApolloChoiceModelling.com>. For more information on choice models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview of the field.

Maintained by Stephane Hess. Last updated 4 days ago.

openblas cpp openmp

14.7 match 1 stars 4.74 score 173 scripts

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

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

Maintained by William Revelle. Last updated 3 months ago.

5.0 match 52 stars 13.94 score 29k scripts 317 dependents

hanase

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.

fortran

15.7 match 4.26 score 18 scripts

dewittpe

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.

cpp

6.7 match 37 stars 9.80 score 448 scripts

rstudio

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 dependents

ikosmidis

brglm2:Bias Reduction in Generalized Linear Models

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

Maintained by Ioannis Kosmidis. Last updated 6 months ago.

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

6.2 match 32 stars 10.41 score 106 scripts 10 dependents

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Paul-Christian Bürkner. Last updated 3 days ago.

bayesian-inference brms multilevel-models stan statistical-models

3.9 match 1.3k stars 16.61 score 13k scripts 34 dependents

gsucarrat

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 dependents

murrayefford

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.

cpp

5.6 match 3 stars 10.18 score 410 scripts 5 dependents

egenn

rtemis:Machine Learning and Visualization

Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.

Maintained by E.D. Gennatas. Last updated 1 months ago.

data-science data-visualization machine-learning machine-learning-library visualization

7.8 match 145 stars 7.09 score 50 scripts 2 dependents

furrer-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-network binomial categorical-data gaussian grouped-datasets mixed-effects multinomial multivariate poisson structure-learning gsl openblas cpp openmp jags

7.8 match 6 stars 6.94 score 90 scripts

poissonconsulting

extras:Helper Functions for Bayesian Analyses

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

Maintained by Nicole Hill. Last updated 2 months ago.

6.3 match 9 stars 8.49 score 15 scripts 16 dependents

lindeloev

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 Lindeløv (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 Lindeløv. Last updated 6 months ago.

jags cpp

7.6 match 106 stars 7.03 score 85 scripts 1 dependents

ghislainv

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.

gsl

8.7 match 9 stars 6.04 score 41 scripts

ddalthorp

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.

cpp

6.7 match 7 stars 7.81 score 55 scripts 2 dependents

cran

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 Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – 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 scripts

mbtyers

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.

jags cpp

8.6 match 5.77 score 91 scripts

novartis

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.

bayesian clinical historical-data meta-analysis cpp

6.3 match 22 stars 7.87 score 115 scripts 4 dependents

ghislainv

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.

gsl openblas cpp

8.4 match 11 stars 5.87 score 68 scripts

bbolker

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 dependents

projectmosaic

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 dependents

distancedevelopment

mrds:Mark-Recapture Distance Sampling

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

Maintained by Laura Marshall. Last updated 2 months ago.

5.8 match 4 stars 8.05 score 78 scripts 7 dependents

bayesiandemography

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.

cpp

8.3 match 5.57 score 49 scripts 1 dependents

lin-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 score

ocbe-uio

contingencytables:Statistical Analysis of Contingency Tables

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

Maintained by Waldir Leoncio. Last updated 7 months ago.

contingency-table

10.7 match 3 stars 4.13 score 8 scripts 1 dependents

projectmosaic

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 dependents

snoweye

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.

openblas

5.8 match 18 stars 7.53 score 123 scripts 2 dependents

nimble-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-inference bayesian-methods hierarchical-models mcmc probabilistic-programming openblas cpp

3.3 match 169 stars 12.97 score 2.6k scripts 19 dependents

bgctw

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 dependents

gamlss-dev

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

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

Maintained by Mikis Stasinopoulos. Last updated 21 days ago.

4.0 match 4 stars 10.50 score 346 scripts 71 dependents

openpharma

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.

jags cpp

5.3 match 21 stars 7.79 score 208 scripts

khvorov45

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 scripts

bquast

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 dependents

suyusung

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 dependents

hwborchers

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 dependents

tidymodels

broom:Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

Maintained by Simon Couch. Last updated 4 months ago.

modeling tidy-data

1.9 match 1.5k stars 21.56 score 37k scripts 1.4k dependents

kkholst

mets:Analysis of Multivariate Event Times

Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.

Maintained by Klaus K. Holst. Last updated 3 days ago.

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

3.0 match 14 stars 13.47 score 236 scripts 42 dependents

cschwarz-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.

jags cpp

6.7 match 1 stars 6.04 score 28 scripts 1 dependents

patriciamar

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.

assessment differential-item-functioning item-analysis item-response-theory psychometrics shiny

5.1 match 44 stars 7.88 score 105 scripts 3 dependents

dgerbing

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 dependents

hesim-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-evaluation microsimulation simulation-modeling cpp

4.9 match 67 stars 8.12 score 41 scripts

cran

Renvlp:Computing Envelope Estimators

Provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

Maintained by Minji Lee. Last updated 1 years ago.

19.9 match 1.98 score 95 scripts

glenmartin31

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 scripts

kingaa

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.

abc b-spline differential-equations dynamical-systems iterated-filtering likelihood likelihood-free markov-chain-monte-carlo markov-model mathematical-modelling measurement-error particle-filter sequential-monte-carlo simulation-based-inference sobol-sequence state-space statistical-inference stochastic-processes time-series openblas

3.3 match 115 stars 11.81 score 1.3k scripts 4 dependents

manuelneumann

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 scripts

bayesball

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 dependents

config-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.

forecasting model-selection model-selection-and-evaluation regression regression-models statistics cpp

3.3 match 30 stars 11.03 score 97 scripts 34 dependents

jandraor

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.

stan system-dynamics

5.6 match 19 stars 6.62 score 62 scripts

wviechtb

metafor:Meta-Analysis Package for R

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

Maintained by Wolfgang Viechtbauer. Last updated 2 days ago.

meta-analysis mixed-effects multilevel-models multivariate

2.3 match 246 stars 16.30 score 4.9k scripts 92 dependents

zpneal

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.

cpp

5.2 match 41 stars 7.06 score 31 scripts 2 dependents

markbravington

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 dependents

rje42

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 dependents

marc-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 dependents

vincentporretta

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 dependents

knudson1

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.

openblas

7.3 match 2 stars 4.64 score 216 scripts

ingebogh

makemyprior:Intuitive Construction of Joint Priors for Variance Parameters

Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, <doi:10.18637/jss.v110.i03>).

Maintained by Ingeborg Hem. Last updated 7 months ago.

6.6 match 1 stars 5.06 score 19 scripts

zhenkewu

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.

bayesian case-control latent-class-analysis jags cpp

5.6 match 8 stars 6.00 score 21 scripts

mauricio1986

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 scripts

giabaio

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-statistics bugs cost-effectiveness-analysis jags tidyverse

10.6 match 2 stars 3.00 score 7 scripts

bristol-vaccine-centre

testerror:Uncertainty in Multiplex Panel Testing

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

Maintained by Robert Challen. Last updated 12 months ago.

9.3 match 1 stars 3.40 score 4 scripts

r-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.

cpp

2.3 match 1 stars 13.94 score 13k scripts 232 dependents

cschwarz-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.

jags cpp

6.7 match 1 stars 4.48 score 12 scripts

philipdelff

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 dependents

nyilin

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 scripts

lbelzile

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 scripts

cran

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 dependents

canmod

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-models epidemiology forecasting mixed-effects model-fitting optimization simulation simulation-modeling cpp

3.3 match 4 stars 8.89 score 246 scripts 1 dependents

smartdata-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-analysis prognosis prognostic-models

3.9 match 7 stars 7.48 score 102 scripts

sdorairaj

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 dependents

arives

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 dependents

nlmixr2

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 dependents

rsparapa

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.

cpp openmp

3.4 match 14 stars 7.96 score 474 scripts 10 dependents

falkcarl

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 scripts

nlmixr2

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.

openblas cpp openmp

3.3 match 9 stars 8.26 score 26 scripts 9 dependents

epiforecasts

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.

cmdstanr effective-reproduction-number-estimation epidemiology infectious-disease-surveillance nowcasting outbreak-analysis pandemic-preparedness real-time-infectious-disease-modelling stan

3.4 match 61 stars 7.88 score 65 scripts

mechantrouquin

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-Removed’) 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 Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.

Maintained by Jean-François Rey. Last updated 6 months ago.

gsl cpp

7.6 match 3.58 score 18 scripts

anestistouloumis

SimCorMultRes:Simulates Correlated Multinomial Responses

Simulates correlated multinomial responses conditional on a marginal model specification.

Maintained by Anestis Touloumis. Last updated 12 months ago.

binary longitudinal-studies multinomial simulation

4.5 match 7 stars 6.04 score 26 scripts 2 dependents

google

imt: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.

cpp

6.9 match 3 stars 3.88 score 6 scripts

epinowcast

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.

cmdstanr effective-reproduction-number-estimation epidemiology infectious-disease-surveillance nowcasting outbreak-analysis pandemic-preparedness real-time-infectious-disease-modelling stan

3.4 match 61 stars 7.79 score 71 scripts

andland

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 scripts

youyifong

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.

openblas

3.3 match 7.87 score 236 scripts 7 dependents

mwheymans

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 scripts

joemolloy

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 scripts

bioc

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.

immunooncology dnamethylation differentialmethylation epigenetics microarray methylationarray multichannel twochannel dataimport normalization preprocessing qualitycontrol

2.0 match 60 stars 12.83 score 996 scripts 26 dependents

theomichelot

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.

cpp

3.9 match 53 stars 6.57 score 64 scripts

riazakhan94

ROCit:Performance Assessment of Binary Classifier with Visualization

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.

Maintained by Md Riaz Ahmed Khan. Last updated 3 years ago.

3.3 match 7.66 score 332 scripts 6 dependents

snoweye

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 scripts

vandomed

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.

aic bmi histograms miscellaneous cpp

4.0 match 1 stars 6.18 score 125 scripts 8 dependents

arnoxieseg

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.

openjdk

10.5 match 2 stars 2.30 score 5 scripts

ecpolley

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 dependents

boehringer-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-methods causal-inference clinical-trial estimand mcmc-methods pharmaceutical-development principal-stratification simulation stan time-to-event cpp

7.6 match 3.18 score

jsocolar

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: Bürkner (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 scripts

myko101

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 scripts

jenswahl

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.

cpp

5.1 match 8 stars 4.60 score

cran

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 scripts

rvlenth

emmeans:Estimated Marginal Means, aka Least-Squares Means

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

Maintained by Russell V. Lenth. Last updated 3 days ago.

1.2 match 377 stars 19.19 score 13k scripts 187 dependents

mjskay

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.

ggplot2 uncertainty uncertainty-visualization visualization cpp

1.5 match 856 stars 15.24 score 3.1k scripts 61 dependents

spluque

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-behavior behavioural-ecology biology diving science

3.3 match 6 stars 6.75 score 55 scripts

dnzmarcio

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.

jags cpp

6.6 match 2 stars 3.30 score 20 scripts

jacob-long

jtools:Analysis and Presentation of Social Scientific Data

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

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

social-sciences

1.5 match 167 stars 14.48 score 4.0k scripts 14 dependents

cdriveraus

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-equations time-series cpp

2.3 match 42 stars 9.58 score 366 scripts 1 dependents

jmm34

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 scripts

simonlabcode

bakR:Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.

Maintained by Isaac Vock. Last updated 4 months ago.

cpp

3.5 match 6 stars 6.12 score 21 scripts

bioc

tidybulk:Brings transcriptomics to the tidyverse

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

Maintained by Stefano Mangiola. Last updated 5 months ago.

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

2.3 match 168 stars 9.48 score 172 scripts 1 dependents

a-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 scripts

nlmixr2

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.

nlmixr2 nonmem pharmacometrics rxode2 cpp

3.3 match 11 stars 6.42 score 23 scripts 1 dependents

rmheiberger

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 dependents

nlmixr2

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 scripts

weberse2

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.

cpp

9.7 match 2.18 score 15 scripts

jamesliley

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 scripts

emmekeaarts

mHMMbayes:Multilevel Hidden Markov Models Using Bayesian Estimation

An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical, normal, or Poisson distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.

Maintained by Emmeke Aarts. Last updated 12 months ago.

cpp

3.4 match 17 stars 6.08 score 35 scripts

ghurault

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-statistics data-analysis statistical-models

7.0 match 2.95 score 18 scripts

taddylab

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-estimation topic-modeling openblas

3.3 match 19 stars 6.27 score 326 scripts 2 dependents

ffqueiroz

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 scripts

chgigot

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.

cpp

3.3 match 15 stars 6.05 score 37 scripts

jmhewitt

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.

cpp

6.3 match 1 stars 3.18 score 10 scripts

mlverse

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 dependents

winvector

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-variables machine-learning-algorithms nested-models prepare-data

1.8 match 285 stars 11.19 score 328 scripts 1 dependents

gogonzo

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 Kałędkowski. Last updated 5 years ago.

cpp

3.3 match 25 stars 5.78 score 24 scripts

limengbinggz

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 scripts

bioc

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.

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

1.8 match 43 stars 10.53 score 190 scripts 6 dependents

friendly

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-visualization generalized-linear-models mosaic-plots

1.8 match 24 stars 10.34 score 472 scripts 3 dependents

izmirlig

pwrFDR:FDR Power

Computing Average and TPX Power under various BHFDR type sequential procedures. All of these procedures involve control of some summary of the distribution of the FDP, e.g. the proportion of discoveries which are false in a given experiment. The most widely known of these, the BH-FDR procedure, controls the FDR which is the mean of the FDP. A lesser known procedure, due to Lehmann and Romano, controls the FDX, or probability that the FDP exceeds a user provided threshold. This is less conservative than FWE control procedures but much more conservative than the BH-FDR proceudre. This package and the references supporting it introduce a new procedure for controlling the FDX which we call the BH-FDX procedure. This procedure iteratively identifies, given alpha and lower threshold delta, an alpha* less than alpha at which BH-FDR guarantees FDX control. This uses asymptotic approximation and is only slightly more conservative than the BH-FDR procedure. Likewise, we can think of the power in multiple testing experiments in terms of a summary of the distribution of the True Positive Proportion (TPP), the portion of tests truly non-null distributed that are called significant. The package will compute power, sample size or any other missing parameter required for power defined as (i) the mean of the TPP (average power) or (ii) the probability that the TPP exceeds a given value, lambda, (TPX power) via asymptotic approximation. All supplied theoretical results are also obtainable via simulation. The suggested approach is to narrow in on a design via the theoretical approaches and then make final adjustments/verify the results by simulation. The theoretical results are described in Izmirlian, G (2020) Statistics and Probability letters, "<doi:10.1016/j.spl.2020.108713>", and an applied paper describing the methodology with a simulation study is in preparation. See citation("pwrFDR").

Maintained by Grant Izmirlian. Last updated 2 months ago.

7.2 match 2.58 score 19 scripts

wojcieko

bhmbasket:Bayesian Hierarchical Models for Basket Trials

Provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.

Maintained by Stephan Wojciekowski. Last updated 3 years ago.

jags cpp

6.6 match 1 stars 2.78 score 5 scripts 1 dependents

cran

genpwr:Power Calculations Under Genetic Model Misspecification

Power and sample size calculations for genetic association studies allowing for misspecification of the model of genetic susceptibility. "Hum Hered. 2019;84(6):256-271.<doi:10.1159/000508558>. Epub 2020 Jul 28." Power and/or sample size can be calculated for logistic (case/control study design) and linear (continuous phenotype) regression models, using additive, dominant, recessive or degree of freedom coding of the genetic covariate while assuming a true dominant, recessive or additive genetic effect. In addition, power and sample size calculations can be performed for gene by environment interactions. These methods are extensions of Gauderman (2002) <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973> and are described in: Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification. American Society of Human Genetics. October 2018, San Diego.

Maintained by Camille Moore. Last updated 4 years ago.

5.6 match 1 stars 3.30 score

ai4ci

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 score

archer-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.

fortran

2.3 match 10 stars 8.12 score 292 scripts 10 dependents

dmphillippo

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.

statistics cpp

2.0 match 35 stars 9.11 score 163 scripts

staffanbetner

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 scripts

jamesliley

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 scripts

american-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 dependents

r-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.

openblas

3.3 match 5.36 score 189 scripts 2 dependents

erhard-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.

bayesian transcriptomics differentialexpression

3.3 match 7 stars 5.32 score 5 scripts 2 dependents

datalorax

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 dependents

cran

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 scripts

kgoldfeld

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-generation data-simulation simulation statistical-models cpp

1.6 match 82 stars 11.00 score 972 scripts 1 dependents

pik-piam

edgeTransport:Prepare EDGE Transport Data for the REMIND model

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

Maintained by Johanna Hoppe. Last updated 3 days ago.

2.5 match 5 stars 6.84 score 16 scripts 2 dependents

jhstaudacher

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 scripts

terminological

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 scripts

statmanrobin

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 scripts

sachsmc

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 scripts

bioc

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.

classification microarray regression

4.0 match 4.04 score 11 scripts

poissonconsulting

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 dependents

kevinmcgregor

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 scripts

melff

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.

survey-data

1.3 match 46 stars 12.34 score 1.2k scripts 13 dependents

jongheepark

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.

cpp

1.6 match 13 stars 9.40 score 2.6k scripts 150 dependents

genentech

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-borrowing psborrow2 simulation-study

1.9 match 18 stars 7.87 score 16 scripts

nlmixr2

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.

monolix nlmixr2 pharmacometrics rxode2 cpp

3.3 match 1 stars 4.47 score 14 scripts 1 dependents

brockk

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.

cpp

1.7 match 41 stars 8.55 score 106 scripts 3 dependents

jonasmoss

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.

density estimation maximum-likelihood

1.8 match 8 stars 8.10 score 62 scripts 7 dependents

deweyme

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 dependents

vladimirholy

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 Vladimír Holý. Last updated 1 years ago.

dcs gas time-series

2.6 match 14 stars 5.45 score 2 scripts

ekstroem

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 Ekstrøm and Sørensen (2014).

Maintained by Claus Ekstrom. Last updated 2 years ago.

5.6 match 2.51 score 108 scripts 1 dependents

drjp

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 scripts

nliulab

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 scripts

cran

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 score

atlas-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.

cpp

3.3 match 4.18 score 3 scripts 1 dependents

marco-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.

cpp

3.3 match 4.10 score 33 scripts 2 dependents

nicolas-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 dependents

ngreifer

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-inference inverse-probability-weights observational-study propensity-scores

1.1 match 112 stars 11.58 score 508 scripts 3 dependents

mezarafael

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 scripts

esm-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 scripts

josie-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 scripts

rstudio

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 scripts

sinafala

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 score

flr

FLSRTMB:FLSR in TMB

Estimates FLR spawner recruitment relationships in TMB

Maintained by Henning Winker. Last updated 15 days ago.

stock-recruit fisheries flr tmb ad cpp

3.4 match 3.67 score 26 scripts 1 dependents

johnaponte

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 scripts

cran

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 dependents

choi-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.

openblas cpp

1.7 match 3 stars 7.34 score 37 scripts 2 dependents

stephenrho

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 scripts

yikeshu0611

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 dependents

julia-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.

openblas cpp

1.9 match 16 stars 6.27 score 29 scripts

pdwaggoner

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 scripts

adamlilith

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-histograms aligned-rank-transform sampling

3.4 match 3.38 score 16 scripts 1 dependents

bioc

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.

dataimport methylseq dnamethylation

1.9 match 6 stars 6.16 score 16 scripts