Showing 17 of total 17 results (show query)
rsquaredacademy
olsrr:Tools for Building OLS Regression Models
Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
Maintained by Aravind Hebbali. Last updated 5 months ago.
collinearity-diagnosticslinear-modelsregressionstepwise-regression
103 stars 12.19 score 1.4k scripts 4 dependents
psychbruce
bruceR:Broadly Useful Convenient and Efficient R Functions
Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
Maintained by Han-Wu-Shuang Bao. Last updated 10 months ago.
anovadata-analysisdata-sciencelinear-modelslinear-regressionmultilevel-modelsstatisticstoolbox
176 stars 7.87 score 316 scripts 3 dependents
tylermorganwall
skpr:Design of Experiments Suite: Generate and Evaluate Optimal Designs
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Maintained by Tyler Morgan-Wall. Last updated 22 days ago.
design-of-experimentslinear-modelslinear-regressionmonte-carlooptimal-designspowersplit-plot-designssurvival-analysiscpp
118 stars 6.89 score 35 scripts
jolars
tactile:New and Extended Plots, Methods, and Panel Functions for 'lattice'
Extensions to 'lattice', providing new high-level functions, methods for existing functions, panel functions, and a theme.
Maintained by Johan Larsson. Last updated 2 years ago.
latticelinear-modelsplottingtime-series
7 stars 6.33 score 154 scripts
pachadotdev
capybara:Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects
Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815> and Gaure (2013) <doi:10.1016/j.csda.2013.03.024>.
Maintained by Mauricio Vargas Sepulveda. Last updated 5 days ago.
cpp11econometricslinear-modelsopenblascppopenmp
13 stars 6.07 score
nelson-gon
manymodelr:Build and Tune Several Models
Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.
Maintained by Nelson Gonzabato. Last updated 10 days ago.
analysis-of-varianceanovacorrelationcorrelation-coefficientgeneralized-linear-modelsgradient-boosting-decision-treesknn-classificationlinear-modelslinear-regressionmachine-learningmissing-valuesmodelsr-programmingrandom-forest-algorithmregression-models
2 stars 5.78 score 50 scripts
rebeccasalles
TSPred:Functions for Benchmarking Time Series Prediction
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Maintained by Rebecca Pontes Salles. Last updated 4 years ago.
benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction
24 stars 5.53 score 94 scripts 1 dependents
ashenoy-cmbi
grafify:Easy Graphs for Data Visualisation and Linear Models for ANOVA
Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.
Maintained by Avinash R Shenoy. Last updated 14 days ago.
ggplot2linear-modelspost-hoc-comparisonsstatisticsvignettes
48 stars 5.31 score 107 scripts
comodin19
BayesVarSel:Bayes Factors, Model Choice and Variable Selection in Linear Models
Bayes factors and posterior probabilities in Linear models, aimed at provide a formal Bayesian answer to testing and variable selection problems.
Maintained by Gonzalo Garcia-Donato. Last updated 3 months ago.
bayesian-methodslinear-modelsgsl
8 stars 5.18 score 26 scripts 1 dependents
nelson-n
lmForc:Linear Model Forecasting
Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.
Maintained by Nelson Rayl. Last updated 7 months ago.
6 stars 5.08 score 20 scripts
lindanab
mecor:Measurement Error Correction in Linear Models with a Continuous Outcome
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Maintained by Linda Nab. Last updated 3 years ago.
linear-modelsmeasurement-errorstatistics
6 stars 5.07 score 13 scripts
bioc
jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data
This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.
Maintained by Melody Jin. Last updated 27 days ago.
spatialgeneexpressiondifferentialexpressionstatisticalmethodtranscriptomicscorrelationlinear-modelsmarker-genesspatial-transcriptomics
2 stars 5.00 score
jared-fowler
prettyglm:Pretty Summaries of Generalized Linear Model Coefficients
One of the main advantages of using Generalised Linear Models is their interpretability. The goal of 'prettyglm' is to provide a set of functions which easily create beautiful coefficient summaries which can readily be shared and explained. 'prettyglm' helps users create coefficient summaries which include categorical base levels, variable importance and type III p.values. 'prettyglm' also creates beautiful relativity plots for categorical, continuous and splined coefficients.
Maintained by Jared Fowler. Last updated 1 years ago.
classificationclassification-modeldata-sciencedata-visualizationglmlinear-modelsregressionregression-analysisregression-modelregression-modelsstatistical-models
3 stars 4.73 score 36 scripts
daniel-gerhard
goric:Generalized Order-Restricted Information Criterion
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.
Maintained by Daniel Gerhard. Last updated 4 years ago.
information-criterionlinear-models
3.81 score 13 scripts
alexmclain
probe:Sparse High-Dimensional Linear Regression with a PaRtitiOned Empirical Bayes Ecm (PROBE) Algorithm
Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. Simulation studies found the all-at-once variant to be superior.
Maintained by Alexander McLain. Last updated 8 months ago.
bayesian-methodshigh-dimensional-datahigh-dimensional-inferencelinear-modelsmachine-learningopenblascppopenmp
1 stars 3.18 score 4 scripts
sparkfish
modelc:A Linear Model to 'SQL' Compiler
This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.
Maintained by Hugo Saavedra. Last updated 5 years ago.
compilergeneralized-linear-modelslinear-modelssqltranspiler
1 stars 2.70 score 1 scripts
quantumofmoose
complexlm:Linear Fitting for Complex Valued Data
Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'.
Maintained by William Ryan. Last updated 1 years ago.
complex-numbersfittinglinear-modelslinear-regressionrobust-statisticsstatistics
1 stars 2.00 score 6 scripts