Showing 66 of total 66 results (show query)
yrosseel
lavaan:Latent Variable Analysis
Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Maintained by Yves Rosseel. Last updated 3 days ago.
factor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modeling
454 stars 16.82 score 8.4k scripts 218 dependents
philchalmers
mirt:Multidimensional Item Response Theory
Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.
Maintained by Phil Chalmers. Last updated 2 days ago.
212 stars 14.93 score 2.5k scripts 40 dependents
bioc
BiocGenerics:S4 generic functions used in Bioconductor
The package defines many S4 generic functions used in Bioconductor.
Maintained by Hervรฉ Pagรจs. Last updated 2 months ago.
infrastructurebioconductor-packagecore-package
12 stars 14.22 score 612 scripts 2.2k dependents
bbolker
bbmle:Tools for General Maximum Likelihood Estimation
Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
Maintained by Ben Bolker. Last updated 1 months ago.
25 stars 13.36 score 1.4k scripts 117 dependents
biodiverse
unmarked:Models for Data from Unmarked Animals
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 10 days ago.
4 stars 13.02 score 652 scripts 12 dependents
alexiosg
rugarch:Univariate GARCH Models
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Maintained by Alexios Galanos. Last updated 3 months ago.
26 stars 12.25 score 1.3k scripts 16 dependentsbioc
variancePartition:Quantify and interpret drivers of variation in multilevel gene expression experiments
Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures.
Maintained by Gabriel E. Hoffman. Last updated 3 months ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
7 stars 11.69 score 1.1k scripts 3 dependents
rkillick
changepoint:Methods for Changepoint Detection
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Maintained by Rebecca Killick. Last updated 4 months ago.
133 stars 11.05 score 736 scripts 40 dependents
r-forge
MatrixModels:Modelling with Sparse and Dense Matrices
Generalized Linear Modelling with sparse and dense 'Matrix' matrices, using modular prediction and response module classes.
Maintained by Martin Maechler. Last updated 12 hours ago.
1 stars 10.91 score 1.5k dependents
bioc
oligo:Preprocessing tools for oligonucleotide arrays
A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).
Maintained by Benilton Carvalho. Last updated 20 days ago.
microarrayonechanneltwochannelpreprocessingsnpdifferentialexpressionexonarraygeneexpressiondataimportzlib
3 stars 10.42 score 528 scripts 10 dependents
drizopoulos
GLMMadaptive:Generalized Linear Mixed Models using Adaptive Gaussian Quadrature
Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.
Maintained by Dimitris Rizopoulos. Last updated 19 days ago.
generalized-linear-mixed-modelsmixed-effects-modelsmixed-models
61 stars 10.37 score 212 scripts 5 dependentsbioc
Cardinal:A mass spectrometry imaging toolbox for statistical analysis
Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.
Maintained by Kylie Ariel Bemis. Last updated 3 months ago.
softwareinfrastructureproteomicslipidomicsmassspectrometryimagingmassspectrometryimmunooncologynormalizationclusteringclassificationregression
48 stars 10.32 score 200 scripts
mages
ChainLadder:Statistical Methods and Models for Claims Reserving in General Insurance
Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.
Maintained by Markus Gesmann. Last updated 2 months ago.
82 stars 10.04 score 196 scripts 2 dependents
flr
FLCore:Core Package of FLR, Fisheries Modelling in R
Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.
Maintained by Iago Mosqueira. Last updated 9 days ago.
fisheriesflrfisheries-modelling
16 stars 8.78 score 956 scripts 23 dependents
pilaboratory
sads:Maximum Likelihood Models for Species Abundance Distributions
Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.
Maintained by Paulo I. Prado. Last updated 1 years ago.
23 stars 8.66 score 244 scripts 3 dependents
actuaryzhang
cplm:Compound Poisson Linear Models
Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <doi:10.1007/s11222-012-9343-7>.
Maintained by Yanwei (Wayne) Zhang. Last updated 1 years ago.
16 stars 8.55 score 75 scripts 10 dependentsbioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
Maintained by Gabriel Hoffman. Last updated 4 days ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
12 stars 8.14 score 128 scriptsbioc
lemur:Latent Embedding Multivariate Regression
Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.
Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.
transcriptomicsdifferentialexpressionsinglecelldimensionreductionregressionopenblascpp
87 stars 7.69 score 81 scripts
nredell
forecastML:Time Series Forecasting with Machine Learning Methods
The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Maintained by Nickalus Redell. Last updated 5 years ago.
deep-learningdirect-forecastingforecastforecastingmachine-learningmulti-step-ahead-forecastingneural-networkpythontime-series
130 stars 7.64 score 134 scriptsbioc
ropls:PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data
Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment).
Maintained by Etienne A. Thevenot. Last updated 5 months ago.
regressionclassificationprincipalcomponenttranscriptomicsproteomicsmetabolomicslipidomicsmassspectrometryimmunooncology
7.55 score 210 scripts 8 dependents
tpetzoldt
growthrates:Estimate Growth Rates from Experimental Data
A collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.
Maintained by Thomas Petzoldt. Last updated 2 years ago.
27 stars 7.52 score 102 scripts
cran
sn:The Skew-Normal and Related Distributions Such as the Skew-t and the SUN
Build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t and the SUN families. For the skew-normal and the skew-t distributions, statistical methods are provided for data fitting and model diagnostics, in the univariate and the multivariate case.
Maintained by Adelchi Azzalini. Last updated 2 years ago.
3 stars 7.44 score 92 dependents
jellegoeman
penalized:L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model
Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.
Maintained by Jelle Goeman. Last updated 3 years ago.
4 stars 7.09 score 429 scripts 17 dependents
sachaepskamp
psychonetrics:Structural Equation Modeling and Confirmatory Network Analysis
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Maintained by Sacha Epskamp. Last updated 2 days ago.
51 stars 6.88 score 41 scripts 1 dependentsflr
FLa4a:A Simple and Robust Statistical Catch at Age Model
A simple and robust statistical Catch at Age model that is specifically designed for stocks with intermediate levels of data quantity and quality.
Maintained by Ernesto Jardim. Last updated 8 days ago.
12 stars 6.71 score 177 scripts 2 dependentscran
fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Analyze and model heteroskedastic behavior in financial time series.
Maintained by Georgi N. Boshnakov. Last updated 1 years ago.
7 stars 6.33 score 51 dependentsbioc
globaltest:Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing
The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms.
Maintained by Jelle Goeman. Last updated 5 months ago.
microarrayonechannelbioinformaticsdifferentialexpressiongopathways
5.89 score 79 scripts 6 dependentsr-forge
fRegression:Rmetrics - Regression Based Decision and Prediction
A collection of functions for linear and non-linear regression modelling. It implements a wrapper for several regression models available in the base and contributed packages of R.
Maintained by Paul J. Northrop. Last updated 9 days ago.
1 stars 5.44 score 23 scripts
anainesvs
pedigreemm:Pedigree-Based Mixed-Effects Models
Fit pedigree-based mixed-effects models.
Maintained by Ana Ines Vazquez. Last updated 1 years ago.
1 stars 5.42 score 87 scripts 2 dependents
beanumber
tidychangepoint:A Tidy Framework for Changepoint Detection Analysis
Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.
Maintained by Benjamin S. Baumer. Last updated 2 months ago.
2 stars 5.30 score 8 scripts
centerforstatistics-ugent
xnet:Two-Step Kernel Ridge Regression for Network Predictions
Fit a two-step kernel ridge regression model for predicting edges in networks, and carry out cross-validation using shortcuts for swift and accurate performance assessment (Stock et al, 2018 <doi:10.1093/bib/bby095> ).
Maintained by Joris Meys. Last updated 4 years ago.
11 stars 5.30 score 12 scripts
covaruber
lme4breeding:Relationship-Based Mixed-Effects Models
Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 1 months ago.
5 stars 5.15 score 7 scriptscran
aod:Analysis of Overdispersed Data
Provides a set of functions to analyse overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).
Maintained by Renaud Lancelot. Last updated 1 years ago.
3 stars 5.15 score 15 dependents
chrisaddy
rrr:Reduced-Rank Regression
Reduced-rank regression, diagnostics and graphics.
Maintained by Chris Addy. Last updated 8 years ago.
10 stars 5.06 score 23 scriptsbioc
fmrs:Variable Selection in Finite Mixture of AFT Regression and FMR Models
The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net.
Maintained by Farhad Shokoohi. Last updated 5 months ago.
survivalregressiondimensionreduction
3 stars 5.00 score 55 scripts 1 dependents
pchausse
momentfit:Methods of Moments
Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Maintained by Pierre Chausse. Last updated 1 years ago.
4.80 score 21 scripts 1 dependentsbioc
frma:Frozen RMA and Barcode
Preprocessing and analysis for single microarrays and microarray batches.
Maintained by Matthew N. McCall. Last updated 5 months ago.
softwaremicroarraypreprocessing
4.72 score 87 scripts 1 dependents
tesselle
kairos:Analysis of Chronological Patterns from Archaeological Count Data
A toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.
Maintained by Nicolas Frerebeau. Last updated 25 days ago.
chronologymatrix-seriationarchaeologyarchaeological-science
4.66 score 11 scripts 1 dependents
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 2 months ago.
8 stars 4.60 score
fcheysson
hawkesbow:Estimation of Hawkes Processes from Binned Observations
Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.
Maintained by Felix Cheysson. Last updated 1 years ago.
7 stars 4.54 score 4 scriptscran
NADA:Nondetects and Data Analysis for Environmental Data
Contains methods described by Dennis Helsel in his book "Nondetects And Data Analysis: Statistics for Censored Environmental Data".
Maintained by Lopaka Lee. Last updated 5 years ago.
2 stars 4.45 score 14 dependents
inbo
inlatools:Diagnostic Tools for INLA Models
Several functions which can be useful to choose sensible priors and diagnose the fitted model.
Maintained by Thierry Onkelinx. Last updated 6 months ago.
bayesian-statisticsgplv3inlamixed-modelsmodel-checkingmodel-validation
4 stars 4.41 score 43 scripts
geobosh
pcts:Periodically Correlated and Periodically Integrated Time Series
Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996) <doi:10.1111/j.1467-9892.1996.tb00281.x>.
Maintained by Georgi N. Boshnakov. Last updated 1 years ago.
par-modelsperiodicperiodic-modelspiar-modelsseasonaltime-seriestime-series-models
3 stars 4.18 score 3 scripts
xiaoruizhu
PAsso:Assessing the Partial Association Between Ordinal Variables
An implementation of the unified framework for assessing partial association between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo Li, Yan Yu and Irini Moustaki (2020) <doi:10.1080/01621459.2020.1796394> Journal of the American Statistical Association). This package provides a set of tools to quantify, visualize, and test partial associations between multiple ordinal variables. It can produce a number of $phi$ measures, partial regression plots, 3-D plots, and p-values for testing H_0: phi=0 or H_0: phi <= delta.
Maintained by Xiaorui (Jeremy) Zhu. Last updated 1 years ago.
association-analysisordinal-variablespartial-associationstatisticscpp
7 stars 4.14 score 13 scripts 1 dependentsbioc
lmdme:Linear Model decomposition for Designed Multivariate Experiments
linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS.
Maintained by Cristobal Fresno. Last updated 5 months ago.
microarrayonechanneltwochannelvisualizationdifferentialexpressionexperimentdatacancer
3.78 score 1 scriptscran
mgwrsar:GWR, Mixed GWR and Multiscale GWR with Spatial Autocorrelation
Functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Maintained by Ghislain Geniaux. Last updated 1 months ago.
7 stars 3.54 score
bbuchsbaum
multivarious:Extensible Data Structures for Multivariate Analysis
Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.
Maintained by Bradley Buchsbaum. Last updated 3 months ago.
3.53 score 17 scripts
sth1402
DynTxRegime:Methods for Estimating Optimal Dynamic Treatment Regimes
Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.
Maintained by Shannon T. Holloway. Last updated 1 years ago.
2 stars 3.44 score 115 scripts 2 dependents
smac-group
ib:Bias Correction via Iterative Bootstrap
An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.
Maintained by Samuel Orso. Last updated 1 years ago.
2 stars 3.36 score 23 scriptssth1402
modelObj:A Model Object Framework for Regression Analysis
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package.
Maintained by Shannon T. Holloway. Last updated 3 years ago.
3.32 score 23 scripts 3 dependents
jchiquet
quadrupen:Sparsity by Worst-Case Quadratic Penalties
Fits classical sparse regression models with efficient active set algorithms by solving quadratic problems as described by Grandvalet, Chiquet and Ambroise (2017) <doi:10.48550/arXiv.1210.2077>. Also provides a few methods for model selection purpose (cross-validation, stability selection).
Maintained by Julien Chiquet. Last updated 9 months ago.
3.18 score 30 scriptsflr
AAP:Aarts and Poos Stock Assessment Model that Estimates Bycatch
FLR version of Aarts and Poos stock assessment model.
Maintained by Iago Mosqueira. Last updated 1 years ago.
2.70 score 5 scriptsflr
bbm:FLR Implementation of a Two-Stage Stock Assessment Model
The two-stage biomass-based model for the Bay of Biscay anchovy (Ibaibarriaga et al., 2008).
Maintained by Leire Ibaibarriaga. Last updated 3 years ago.
2.70 score 5 scriptscran
mvngGrAd:Moving Grid Adjustment in Plant Breeding Field Trials
Package for moving grid adjustment in plant breeding field trials.
Maintained by Frank Technow. Last updated 1 years ago.
2.30 scorecran
twopartm:Two-Part Model with Marginal Effects
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Maintained by Yajie Duan. Last updated 2 years ago.
3 stars 2.18 score
ffqueiroz
robustbetareg:Robust Beta Regression
Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2022) <arXiv:2209.11315>.
Maintained by Felipe Queiroz. Last updated 2 years ago.
1.70 score 5 scripts
cran
eDMA:Dynamic Model Averaging with Grid Search
Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
Maintained by Leopoldo Catania. Last updated 7 years ago.
1.70 scorecran
truncSP:Semi-parametric estimators of truncated regression models
Semi-parametric estimation of truncated linear regression models
Maintained by Anita Lindmark. Last updated 11 years ago.
1.48 score 1 dependents
bpfaff
gogarch:Generalized Orthogonal GARCH (GO-GARCH) Models
Provision of classes and methods for estimating generalized orthogonal GARCH models. This is an alternative approach to CC-GARCH models in the context of multivariate volatility modeling.
Maintained by Bernhard Pfaff. Last updated 3 years ago.
1.26 score 18 scripts
davidemassidda
rAverage:Parameter Estimation for the Averaging Model of Information Integration Theory
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
Maintained by Davide Massidda. Last updated 8 years ago.
1.20 score 16 scriptsjchiquet
spinyReg:Sparse Generative Model and Its EM Algorithm
Implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood.
Maintained by Julien Chiquet. Last updated 10 years ago.
1.00 score 1 scriptscran
cpop:Detection of Multiple Changes in Slope in Univariate Time-Series
Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) <doi:10.1080/10618600.2018.1512868>. This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) <doi:10.18637/jss.v109.i07>.
Maintained by Daniel Grose. Last updated 10 months ago.
1.00 score