palasso - Paired Lasso Regression

Implements sparse regression with paired covariates (Rauschenberger et al. 2020 <doi:10.1007/s11634-019-00375-6>). For the optional shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).

Last updated 7 months ago

1 stars 1.87 score 10 dependencies 5 dependents

cornet - Elastic Net with Dichotomised Outcomes

Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2023, <doi:10.1080/02664763.2023.2233057>). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.

Last updated 1 years ago

2 stars 1.54 score 11 dependencies 3 dependents

transreg - Penalised Regression with Multiple Sets of Prior Effects

Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette, install 'fwelnet' from 'GitHub' <https://github.com/kjytay/fwelnet>.

Last updated 10 months ago

1.47 score 14 dependencies

starnet - Stacked Elastic Net

Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

Last updated 3 years ago

5 stars 1.33 score 12 dependencies 1 dependents

joinet - Multivariate Elastic Net Regression

Implements high-dimensional multivariate regression by stacked generalisation (Rauschenberger 2021 <doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).

Last updated 3 years ago

4 stars 1.27 score 12 dependencies 1 dependents

globalSeq - Global Test for Counts

The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data.

Last updated 4 months ago

geneexpressionexonarraydifferentialexpressiongenomewideassociationtranscriptomicsdimensionreductionregressionsequencingwholegenomernaseqexomeseqmirnamultiplecomparison

1.08 score 0 dependencies

semisup - Semi-Supervised Mixture Model

Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.

Last updated 4 months ago

snpgenomicvariationsomaticmutationgeneticsclassificationclusteringdnaseqmicroarraymultiplecomparison

1 stars 0.82 score 1 dependencies