Showing 13 of total 13 results (show query)
merliseclyde
BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Maintained by Merlise Clyde. Last updated 4 months ago.
bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas
44 stars 10.63 score 420 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 7 months ago.
adjusted-score-equationsalgorithmsbias-reducing-adjustmentsbias-reductionestimationglmlogistic-regressionnominal-responsesordinal-responsesregressionregression-algorithmsstatistics
32 stars 10.41 score 106 scripts 10 dependents
eheinzen
elo:Ranking Teams by Elo Rating and Comparable Methods
A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models.
Maintained by Ethan Heinzen. Last updated 1 years ago.
eloelo-ratinglogistic-regressionmarkov-chainmarkov-modelrankingsports-analyticscpp
37 stars 7.05 score 153 scripts
doccstat
fastcpd:Fast Change Point Detection via Sequential Gradient Descent
Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.
Maintained by Xingchi Li. Last updated 11 days ago.
change-point-detectioncppcustom-functiongradient-descentlassolinear-regressionlogistic-regressionofflinepeltpenalized-regressionpoisson-regressionquasi-newtonstatisticstime-serieswarm-startfortranopenblascppopenmp
22 stars 7.00 score 7 scripts
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-regressionmultinomial-logistic-regressionpolytomous-variables
10 stars 6.04 score 33 scripts
rsquaredacademy
rbin:Tools for Binning Data
Manually bin data using weight of evidence and information value. Includes other binning methods such as equal length, quantile and winsorized. Options for combining levels of categorical data are also available. Dummy variables can be generated based on the bins created using any of the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.
Maintained by Aravind Hebbali. Last updated 5 months ago.
binninglogistic-regressionwoe-and-ivwoebinning
13 stars 5.54 score 53 scripts
craig-parylo
plotor:Produces an Odds Ratio Plot from a Logistic Regression Model
Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95% confidence interval and plot the results using 'ggplot2'.
Maintained by Craig Parylo. Last updated 12 days ago.
ggplot2glmlogistic-regressionodds-ratio
2 stars 5.04 score 7 scripts
sfcheung
betaselectr:Betas-Select in Structural Equation Models and Linear Models
It computes betas-select, coefficients after standardization in structural equation models and regression models, standardizing only selected variables. Supports models with moderation, with product terms formed after standardization. It also offers confidence intervals that account for standardization, including bootstrap confidence intervals as proposed by Cheung et al. (2022) <doi:10.1037/hea0001188>.
Maintained by Shu Fai Cheung. Last updated 5 months ago.
bootstrappingconfidence-intervalsgeneralized-linear-modelslavaanlogistic-regressionregressionsemstandardizationstructural-equation-modeling
1 stars 4.95 score 8 scripts
tjetka
SLEMI:Statistical Learning Based Estimation of Mutual Information
The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.
Maintained by Tomasz Jetka. Last updated 1 years ago.
channel-capacityinformation-theorylogistic-regressionmutual-information-estimation
4 stars 4.92 score 21 scripts
rudolfjagdhuber
ExhaustiveSearch:A Fast and Scalable Exhaustive Feature Selection Framework
The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant.
Maintained by Rudolf Jagdhuber. Last updated 4 years ago.
aicexhaustive-searchfeature-selectionlinear-regressionlogistic-regressionmachine-learningmodel-selectionmseopenblascpp
4 stars 3.60 score 5 scripts
youjin1207
logisticRR:Adjusted Relative Risk from Logistic Regression
Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
Maintained by Youjin Lee. Last updated 5 years ago.
logistic-regressionodds-ratiorelative-risk
3 stars 3.38 score 16 scripts
jrenstat
regnet:Network-Based Regularization for Generalized Linear Models
Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.
Maintained by Jie Ren. Last updated 2 months ago.
cancer-prognosislogistic-regressionnetworkpenalized-regressionregularizationsurvivalopenblascppopenmp
4 stars 3.30 score
ejikeugba
gofcat:Goodness-of-Fit Measures for Categorical Response Models
A post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) <http://www.statmod.org/workshops_archive_proceedings_2018.html>.
Maintained by Ejike R. Ugba. Last updated 2 years ago.
brant-testbrier-scoreshosmer-lemeshow-testlikelihood-ratio-testlipsitz-testlog-loss-score-metriclogistic-regressionmisclassificationordinal-regressionproportional-odds-testpseudo-r2pulkstenis-robinson-test
2 stars 3.18 score 15 scripts