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cran
multiway:Component Models for Multi-Way Data
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Maintained by Nathaniel E. Helwig. Last updated 6 years ago.
57.5 match 3 stars 2.58 score 4 dependents
grvanderploeg
parafac4microbiome:Parallel Factor Analysis Modelling of Longitudinal Microbiome Data
Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.
Maintained by Geert Roelof van der Ploeg. Last updated 20 days ago.
dimensionality-reductionmicrobiomemicrobiome-datamultiwaymultiway-algorithmsparallel-factor-analysis
18.5 match 6 stars 6.31 score 13 scripts
danielquiroz97
RGCxGC:Preprocessing and Multivariate Analysis of Bidimensional Gas Chromatography Data
Toolbox for chemometrics analysis of bidimensional gas chromatography data. This package import data for common scientific data format (NetCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated.
Maintained by Cristian Quiroz-Moreno. Last updated 2 years ago.
chemphyschemometricsgcxgcmultiway-algorithmspreprocessing
12.8 match 7 stars 4.77 score 17 scripts
dcgerard
tensr:Covariance Inference and Decompositions for Tensor Datasets
A collection of functions for Kronecker structured covariance estimation and testing under the array normal model. For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
Maintained by David Gerard. Last updated 2 years ago.
7.0 match 5 stars 6.53 score 56 scripts 4 dependents
r-forge
partykit:A Toolkit for Recursive Partytioning
A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
Maintained by Torsten Hothorn. Last updated 4 days ago.
2.0 match 12.71 score 2.3k scripts 97 dependents
s3alfisc
fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models
Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorslinear-regression-modelswild-bootstrapwild-cluster-bootstrapopenblascppopenmp
3.2 match 24 stars 6.67 score 109 scripts 2 dependents
doubleml
DoubleML:Double Machine Learning in R
Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible. More information available in the publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.
Maintained by Philipp Bach. Last updated 4 months ago.
causal-inferencedata-sciencedouble-machine-learningeconometricsmachine-learningmlr3statistics
1.6 match 137 stars 9.17 score 267 scripts 1 dependents
wjbraun
DAAG:Data Analysis and Graphics Data and Functions
Functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.
Maintained by W. John Braun. Last updated 11 months ago.
1.7 match 8.25 score 1.2k scripts 1 dependents
valentint
rrcov3way:Robust Methods for Multiway Data Analysis, Applicable also for Compositional Data
Provides methods for multiway data analysis by means of Parafac and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).
Maintained by Valentin Todorov. Last updated 1 years ago.
3.3 match 4.28 score 38 scripts
matthiaspucher
staRdom:PARAFAC Analysis of EEMs from DOM
'This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.'
Maintained by Matthias Pucher. Last updated 4 months ago.
1.7 match 21 stars 6.03 score 86 scripts
midfieldr
midfieldr:Tools and Methods for Working with MIDFIELD Data in 'R'
Provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in 'R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. 'midfieldr' interacts with practice data provided in 'midfielddata', an R data package available at <https://midfieldr.github.io/midfielddata/>. 'midfieldr' also interacts with the full MIDFIELD database for users who have access. This work is supported by the US National Science Foundation through grant numbers 1545667 and 2142087.
Maintained by Richard Layton. Last updated 2 months ago.
1.8 match 2 stars 5.56 score 26 scripts
amcgarvey93
easypower:Sample Size Estimation for Experimental Designs
Power analysis is used in the estimation of sample sizes for experimental designs. Most programs and R packages will only output the highest recommended sample size to the user. Often the user input can be complicated and computing multiple power analyses for different treatment comparisons can be time consuming. This package simplifies the user input and allows the user to view all of the sample size recommendations or just the ones they want to see. The calculations used to calculate the recommended sample sizes are from the 'pwr' package.
Maintained by Aaron McGarvey. Last updated 1 years ago.
3.6 match 1 stars 2.48 score 4 scripts 1 dependents
edsandorf
spdesign:Designing Stated Preference Experiments
Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).
Maintained by Erlend Dancke Sandorf. Last updated 5 months ago.
1.3 match 4.60 score 20 scripts
atravert
RcmdrPlugin.aRnova:R Commander Plug-in for Repeated-Measures ANOVA
R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. It adds a new menu entry for repeated measures that allows to deal with up to three within-subject factors and optionally with one or several between-subject factors. It also provides supplementary options to oneWayAnova() and multiWayAnova() functions, such as choice of ANOVA type, display of effect sizes and post hoc analysis for multiWayAnova().
Maintained by Arnaud Travert. Last updated 7 years ago.
2.3 match 2.00 score 2 scriptscran
modMax:Community Structure Detection via Modularity Maximization
The algorithms implemented here are used to detect the community structure of a network. These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Maintained by Maria Schelling. Last updated 10 years ago.
3.3 match 2 stars 1.30 score 10 scripts
yzeng58
tensorsparse:Multiway Clustering via Tensor Block Models
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
Maintained by Yuchen Zeng. Last updated 4 years ago.
3.7 match 1.00 score 2 scriptscran
MultiwayRegression:Perform Tensor-on-Tensor Regression
Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>]. Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.
Maintained by Eric F. Lock. Last updated 6 years ago.
1.7 match 1.48 score 1 dependents
jhu267
dTBM:Multi-Way Spherical Clustering via Degree-Corrected Tensor Block Models
Implement weighted higher-order initialization and angle-based iteration for multi-way spherical clustering under degree-corrected tensor block model. See reference Jiaxin Hu and Miaoyan Wang (2023) <doi:10.1109/TIT.2023.3239521>.
Maintained by Jiaxin Hu. Last updated 2 years ago.
1.7 match 1.00 scorecran
PTAk:Principal Tensor Analysis on k Modes
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
Maintained by Didier G. Leibovici. Last updated 2 years ago.
0.8 match 1 stars 1.30 score
jhhmuc
confreq:Configural Frequencies Analysis Using Log-Linear Modeling
Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
Maintained by Joerg-Henrik Heine. Last updated 2 years ago.
0.5 match 1.52 score 33 scripts