• globaltest

  • Referenced in 2 articles [sw19391]
  • large sets of variables. In high-dimensional omics studies where multiple molecular profiles are obtained ... single univariate response, to allow high-dimensional multivariate response. We apply the method to several...
  • CMARS

  • Referenced in 28 articles [sw08523]
  • MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions ... predictors are allowed to determine the response variable. The MARS method consists of two parts...
  • ADMMFTIRE

  • Referenced in 1 article [sw41249]
  • been given to the multivariate response with high-dimensional covariates settings. To fill...
  • SPaVS

  • Referenced in 1 article [sw42214]
  • units has raised interest in relating high-dimensional data sets. This problem can be framed ... with one data set treated as a response matrix and the other ... covariate matrix. The high-dimensionality of both the response and covariate matrices along with...
  • ordinalgmifs

  • Referenced in 6 articles [sw14365]
  • ordinalgmifs: Ordinal Regression for High-dimensional Data. This package provides a function for fitting cumulative ... backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds...
  • countgmifs

  • Referenced in 1 article [sw37934]
  • package countgmifs: Discrete Response Regression for High-Dimensional Data. Provides a function for fitting Poisson...
  • QSHEP5D

  • Referenced in 7 articles [sw04406]
  • Algorithm 798: High-dimensional interpolation using the modified Shepard method A new implementation ... need for interpolated 5D hypervolumes of environmental response variables produced by forest growth and production...
  • PEER

  • Referenced in 1 article [sw40511]
  • outcomes, large number of responses, and high dimensionality in predictors poses unprecedented challenges in estimation ... both the numbers of responses and predictors can be high-dimensional. Motivated by sparse factor...
  • care

  • Referenced in 7 articles [sw19441]
  • approach of Zuber and Strimmer (2011) ”High-dimensional regression and variable selection using CAR scores ... scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared...
  • aPC

  • Referenced in 1 article [sw40982]
  • obtain a model response surface in the form of a high-dimensional polynomial in uncertain...
  • mirtjml

  • Referenced in 2 articles [sw36870]
  • mirtjml: Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis. Provides constrained joint maximum ... factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions ... parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods ... Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika...
  • CorrT

  • Referenced in 6 articles [sw26432]
  • sparse high-dimensional linear models. In high-dimensional linear models, the sparsity assumption is typically ... expected to be associated with the response, indicating that possibly all, rather than just...
  • nodeHarvest

  • Referenced in 2 articles [sw33154]
  • simple interpretable tree-like estimator for high-dimensional regression and classification. A few nodes ... nodes and predictions are the weighted average response across all these groups. The package offers...
  • tilting

  • Referenced in 1 article [sw39810]
  • algorithm for variable selection in high-dimensional linear regression using the ”tilted correlation ... contribution of each variable to the response which takes into account high correlations among...
  • BFDA

  • Referenced in 2 articles [sw14769]
  • functional independent variable, scalar and functional response variables are provided. The advantages of BFDA include ... scale functional data with random or high-dimensional observation-grids; (3) Flexibly adapts for both...
  • SW1PerS

  • Referenced in 10 articles [sw17206]
  • evaluating the circularity of a high-dimensional representation of the signal. SW1PerS is compared ... consistently classify damped signals as highly periodic. On biological data, and for several experiments ... Yeast metabolic cycle which are highly-ranked only by SW1PerS, contains evidently non-cosine patterns ... ECM33, CDC9, SAM1,2 and MSH6) with highly periodic expression profiles. In data from...
  • ebreg

  • Referenced in 1 article [sw35484]
  • Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical ... variable selection, and prediction of a future response. The method was first presented in Martin...
  • Superheat

  • Referenced in 5 articles [sw19761]
  • typically do not scale well in high-dimensional settings. An existing visualization technique that ... types simultaneously, adding to the heatmap a response variable as a scatterplot, model results...
  • PottsMix

  • Referenced in 1 article [sw32783]
  • correspond to active variables in a high-dimensional dynamic linear model. The methodology is investigated ... demonstrated on an application examining the neural response to the perception of scrambled faces...
  • CoRF

  • Referenced in 2 articles [sw42227]
  • data moderated randomForest. Paper: Improved high-dimensional prediction with Random Forests ... primary data, but does not use its response labels. These moderated sampling probabilities are, inspired...