R-universe search: needs:robustHD

Showing 8 of total 8 results (show query)

matthias-da

robCompositions:Compositional Data Analysis

Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Maintained by Matthias Templ. Last updated 1 months ago.

cpp

11 stars 9.21 score 226 scripts 2 dependents

zhenkewu

baker:"Nested Partially Latent Class Models"

Provides functions to specify, fit and visualize nested partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>) for inference of population disease etiology and individual diagnosis. In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package.

Maintained by Zhenke Wu. Last updated 11 months ago.

bayesian case-control latent-class-analysis jags cpp

8 stars 6.00 score 21 scripts

fatmasevinck

enetLTS:Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression

Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation.

Maintained by Fatma Sevinc KURNAZ. Last updated 2 years ago.

3 stars 4.07 score 26 scripts

valentint

rrcovHD:Robust Multivariate Methods for High Dimensional Data

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

Maintained by Valentin Todorov. Last updated 7 months ago.

cpp

3.39 score 49 scripts

cran

PAMhm:Generate Heatmaps Based on Partitioning Around Medoids (PAM)

Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the 'cluster' package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>. Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.

Maintained by Vidal Fey. Last updated 4 years ago.

2.00 score

aalfons

sparseLTSEigen:RcppEigen back end for sparse least trimmed squares regression

Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models.

Maintained by Andreas Alfons. Last updated 10 years ago.

cpp openmp

1 stars 2.00 score 1 scripts

takayukikawashima

gamreg:Robust and Sparse Regression via Gamma-Divergence

Robust regression via gamma-divergence with L1, elastic net and ridge.

Maintained by Takayuki Kawashima. Last updated 7 years ago.

cpp

1.00 score 5 scripts

buybnb

CMMs:Compositional Mediation Model

A compositional mediation model for continuous outcome and binary outcomes to deal with mediators that are compositional data. Lin, Ziqiang et al. (2022) <doi:10.1016/j.jad.2021.12.019>.

Maintained by Ziqiang Lin. Last updated 2 years ago.

1.00 score