Showing 55 of total 55 results (show query)
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.
93 stars 13.32 score 7.2k scripts 7 dependents
bioc
mzR:parser for netCDF, mzXML and mzML and mzIdentML files (mass spectrometry data)
mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS.
Maintained by Steffen Neumann. Last updated 2 months ago.
immunooncologyinfrastructuredataimportproteomicsmetabolomicsmassspectrometryzlibcpp
45 stars 12.77 score 204 scripts 44 dependents
greta-dev
greta:Simple and Scalable Statistical Modelling in R
Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.
Maintained by Nicholas Tierney. Last updated 19 days ago.
566 stars 12.53 score 396 scripts 6 dependents
tidyverts
fabletools:Core Tools for Packages in the 'fable' Framework
Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Maintained by Mitchell OHara-Wild. Last updated 2 months ago.
91 stars 12.18 score 396 scripts 18 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 1 months ago.
40 stars 11.24 score 220 scripts 13 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 10 days ago.
fisheriesflrfisheries-modelling
16 stars 8.78 score 956 scripts 23 dependentsnlmixr2
nlmixr2:Nonlinear Mixed Effects Models in Population PK/PD
Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Maintained by Matthew Fidler. Last updated 1 months ago.
52 stars 8.38 score 120 scripts 3 dependents
swihart
rmutil:Utilities for Nonlinear Regression and Repeated Measurements Models
A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at <https://www.commanster.eu/rcode.html>).
Maintained by Bruce Swihart. Last updated 2 years ago.
1 stars 8.35 score 358 scripts 70 dependentsnlmixr2
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 12 days ago.
9 stars 8.33 score 26 scripts 9 dependents
robinhankin
emulator:Bayesian Emulation of Computer Programs
Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.
Maintained by Robin K. S. Hankin. Last updated 9 months ago.
4 stars 8.27 score 56 scripts 17 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 13 days ago.
6 stars 7.42 score 394 scripts 3 dependents
robjhyndman
vital:Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data
Analysing vital statistics based on tools consistent with the tidyverse. Tools are provided for data visualization, life table calculations, computing net migration numbers, Lee-Carter modelling; functional data modelling and forecasting.
Maintained by Rob Hyndman. Last updated 2 days ago.
28 stars 7.20 score 18 scripts
jacobbien
simulator:An Engine for Running Simulations
A framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at <arXiv:1607.00021>.
Maintained by Jacob Bien. Last updated 2 years ago.
52 stars 7.13 score 103 scriptsflr
mse:Tools for Running Management Strategy Evaluations using FLR
A set of functions and methods to enable the development and running of Management Strategy Evaluation (MSE) analyses, using the FLR packages and classes and the a4a methods and algorithms.
Maintained by Iago Mosqueira. Last updated 1 months ago.
4 stars 6.99 score 137 scripts 3 dependents
asael697
bayesforecast:Bayesian Time Series Modeling with Stan
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.
bayesian-inferenceforecasting-modelsmcmcstantime-series-analysiscpp
45 stars 6.92 score 62 scriptsbioc
scClassify:scClassify: single-cell Hierarchical Classification
scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.
Maintained by Yingxin Lin. Last updated 5 months ago.
singlecellgeneexpressionclassification
23 stars 6.92 score 30 scriptsbioc
MPRAnalyze:Statistical Analysis of MPRA data
MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes.
Maintained by Tal Ashuach. Last updated 5 months ago.
immunooncologysoftwarestatisticalmethodsequencinggeneexpressioncellbiologycellbasedassaysdifferentialexpressionexperimentaldesignclassification
12 stars 6.86 score 30 scriptsflr
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 9 days ago.
12 stars 6.71 score 177 scripts 2 dependents
rjdverse
rjd3sts:State Space Framework and Structural Time Series with 'JDemetra+ 3.x'
R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.
Maintained by Jean Palate. Last updated 9 months ago.
2 stars 6.64 score 25 scripts 4 dependents
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.
nlmixr2nonmempharmacometricsrxode2cpp
12 stars 6.46 score 23 scripts 1 dependents
fabrice-rossi
mixvlmc:Variable Length Markov Chains with Covariates
Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.
Maintained by Fabrice Rossi. Last updated 11 months ago.
machine-learningmarkov-chainmarkov-modelstatisticstime-seriescpp
2 stars 6.23 score 20 scriptsbioc
struct:Statistics in R Using Class-based Templates
Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates.
Maintained by Gavin Rhys Lloyd. Last updated 5 months ago.
5.91 score 76 scripts 3 dependentsnlmixr2
nlmixr2extra:Nonlinear Mixed Effects Models in Population PK/PD, Extra Support Functions
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>). This package is for support functions like preconditioned fits <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate selection.
Maintained by Matthew Fidler. Last updated 1 months ago.
3 stars 5.83 score 11 scripts 5 dependents
centerforstatistics-ugent
pim:Fit Probabilistic Index Models
Fit a probabilistic index model as described in Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the modeling function has changed in this new version. The old version is still available at R-Forge.
Maintained by Joris Meys. Last updated 3 months ago.
10 stars 5.33 score 43 scriptsbioc
coseq:Co-Expression Analysis of Sequencing Data
Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided.
Maintained by Andrea Rau. Last updated 5 months ago.
geneexpressionrnaseqsequencingsoftwareimmunooncology
4.98 score 16 scripts
fmenchetti
CausalMBSTS:MBSTS Models for Causal Inference and Forecasting
Infers the causal effect of an intervention on a multivariate response through the use of Multivariate Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>. The package also includes functions for model building and forecasting.
Maintained by Fiammetta Menchetti. Last updated 4 years ago.
16 stars 4.90 score 6 scripts
diegommcc
SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
Maintained by Diego Mañanes. Last updated 5 months ago.
deconvolutiondeep-learningneural-networkspatial-transcriptomics
5 stars 4.88 score 1 scriptsbioc
rsbml:R support for SBML, using libsbml
Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models.
Maintained by Michael Lawrence. Last updated 1 months ago.
graphandnetworkpathwaysnetworklibsbmlcpp
4.71 score 19 scripts 1 dependents
tianxia-jia
mcgf:Markov Chain Gaussian Fields Simulation and Parameter Estimation
Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.
Maintained by Tianxia Jia. Last updated 9 months ago.
1 stars 4.64 score 11 scripts
poissonconsulting
embr:Model Builder Utility Functions and Virtual Classes
Utility functions and virtual classes shared by model builder packages such as tmbr, jmbr and smbr.
Maintained by Joe Thorley. Last updated 2 months ago.
3 stars 4.61 score 4 scripts 3 dependentsbioc
zitools:Analysis of zero-inflated count data
zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.
Maintained by Carlotta Meyring. Last updated 5 months ago.
softwarestatisticalmethodmicrobiome
4.60 score 6 scriptsbioc
MAIT:Statistical Analysis of Metabolomic Data
The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions.
Maintained by Pol Sola-Santos. Last updated 5 months ago.
immunooncologymassspectrometrymetabolomicssoftware
4.60 score 20 scripts
hredestig
crmn:CCMN and Other Normalization Methods for Metabolomics Data
Implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry https://doi.org/10.1021/ac901143w and other normalization algorithms.
Maintained by Henning Redestig. Last updated 5 years ago.
1 stars 4.59 score 24 scripts 1 dependents
mrc-ide
gonovax:Deterministic Compartmental Model of Gonorrhoea with Vaccination
Model for gonorrhoea vaccination, using odin.
Maintained by Lilith Whittles. Last updated 16 days ago.
3 stars 4.56 score
shah-in-boots
rmdl:A Causality-Informed Modeling Approach
A system for describing and manipulating the many models that are generated in causal inference and data analysis projects, as based on the causal theory and criteria of Austin Bradford Hill (1965) <doi:10.1177/003591576505800503>. This system includes the addition of formal attributes that modify base `R` objects, including terms and formulas, with a focus on variable roles in the "do-calculus" of modeling, as described in Pearl (2010) <doi:10.2202/1557-4679.1203>. For example, the definition of exposure, outcome, and interaction are implicit in the roles variables take in a formula. These premises allow for a more fluent modeling approach focusing on variable relationships, and assessing effect modification, as described by VanderWeele and Robins (2007) <doi:10.1097/EDE.0b013e318127181b>. The essential goal is to help contextualize formulas and models in causality-oriented workflows.
Maintained by Anish S. Shah. Last updated 10 months ago.
epidemiologymodelingstatistics
4.54 score 7 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.
monolixnlmixr2pharmacometricsrxode2cpp
1 stars 4.40 score 14 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 scriptsbioc
MVCClass:Model-View-Controller (MVC) Classes
Creates classes used in model-view-controller (MVC) design
Maintained by Elizabeth Whalen. Last updated 5 months ago.
visualizationinfrastructuregraphandnetwork
3.78 score 1 dependentsbioc
segmenter:Perform Chromatin Segmentation Analysis in R by Calling ChromHMM
Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation.
Maintained by Mahmoud Ahmed. Last updated 5 months ago.
softwarehistonemodificationbioconductorchromhmmsegmentation-an
4 stars 3.60 score 9 scripts
urniaz
ai:Build, Predict and Analyse Artificial Intelligence Models
An interface for data processing, building models, predicting values and analysing outcomes. Fitting Linear Models, Robust Fitting of Linear Models, k-Nearest Neighbor Classification, 1-Nearest Neighbor Classification, and Conditional Inference Trees are available.
Maintained by Rafal Urniaz. Last updated 6 months ago.
1 stars 3.49 score 31 scripts
tdjorgensen
simsem:SIMulated Structural Equation Modeling
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
Maintained by Terrence D. Jorgensen. Last updated 4 years ago.
3.40 score 276 scripts
sth1402
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
ldecicco-usgs
rloadest:River Load Estimation
Collection of functions to make constituent load estimations based on the LOADEST program.
Maintained by Dave Lorenz. Last updated 4 years ago.
3.29 score 98 scriptsflr
FLFishery:Classes and Methods for Simpler Fleet/Fishery Modelling
A set of classes and methods for modelling of fleet dynamics. Fisheries are groups of vessels sharing an effort time series, with static or changing spatio-temporal patterns in selectivity.
Maintained by Iago Mosqueira. Last updated 4 months ago.
3.02 score 8 scripts 7 dependents
mrcieu
varGWASR:Least Absolute Deviation Regression Brown Forsythe Test
Brown-Forsythe SNP test using LAD regression and variance effect estimate
Maintained by Matthew Lyon. Last updated 3 years ago.
geneticsheteroscedasticityheteroskedasticitystatisticsvariance
1 stars 2.70 score
mestherlv
mme:Multinomial Mixed Effects Models
Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing independent time effect; Model 3, introducing correlated time effect. mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
Maintained by E. Lopez-Vizcaino. Last updated 6 years ago.
1 stars 2.59 score 39 scripts
cedricbriandgithub
stacomiR:Fish Migration Monitoring
Graphical outputs and treatment for a database of fish pass monitoring. It is a part of the 'STACOMI' open source project developed in France by the French Office for Biodiversity institute to centralize data obtained by fish pass monitoring. This version is available in French and English. See <http://stacomir.r-forge.r-project.org/> for more information on 'STACOMI'.
Maintained by Cedric Briand. Last updated 1 years ago.
1 stars 2.43 score 27 scripts
rahim69
Brq:Bayesian Analysis of Quantile Regression Models
Bayesian estimation and variable selection for quantile regression models.
Maintained by Rahim Alhamzawi (University of Al-Qadisiyah). Last updated 5 years ago.
4 stars 2.11 score 32 scripts
cran
pdR:Threshold Model and Unit Root Tests in Cross-Section and Time Series Data
Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.
Maintained by Ho Tsung-wu. Last updated 7 months ago.
4 stars 1.90 score
umatter
TheOpenAIR:Integrate 'OpenAI' Large Language Models into Your 'R' Workflows
Utilizing the 'OpenAI' API as the back end (<https://platform.openai.com/docs/api-reference>), 'TheOpenAIR' offers 'R' wrapper functions for the 'ChatGPT' endpoint and several high-level functions that enable the integration of 'ChatGPT' capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.
Maintained by Ulrich Matter. Last updated 2 years ago.
1 stars 1.70 score 6 scripts
fvcampos
GGClassification:Gabriel Graph Based Large-Margin Classifiers
Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.
Maintained by Felipe Campos. Last updated 5 years ago.
1.00 scorecran
StatRec:A Statistical Method for Multi-Item Rating and Recommendation Problems
Implements the methodological developments found in Hermes (2025) <doi:10.48550/arXiv.2503.02786>, and allows for the statistical modeling of data consisting of multiple users that provide an ordinal rating for one or multiple items.
Maintained by Sjoerd Hermes. Last updated 11 days ago.
1.00 score