scDiagnostics - Cell type annotation diagnostics
The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.
Last updated 3 months ago
annotationclassificationclusteringgeneexpressionrnaseqsinglecellsoftwaretranscriptomics
7.80 score 8 stars 46 scripts 136 downloadsRPEIF - Computation and Plots of Influence Functions for Risk and Performance Measures
Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.
Last updated 1 months ago
4.89 score 1 stars 2 dependents 13 scripts 628 downloadsRPEGLMEN - Gamma and Exponential Generalized Linear Models with Elastic Net Penalty
Implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <arxiv:1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
Last updated 5 months ago
cpp
4.18 score 1 dependents 3 scripts 672 downloadsRPESE - Estimates of Standard Errors for Risk and Performance Measures
Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.
Last updated 5 months ago
3.70 score 9 scripts 872 downloadsSplitGLM - Split Generalized Linear Models
Functions to compute split generalized linear models. The approach fits generalized linear models that split the covariates into groups. The optimal split of the variables into groups and the regularized estimation of the coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. Example applications can be found in Christidis et al. (2021) <arXiv:2102.08591>.
Last updated 5 months ago
cppopenmp
3.18 score 1 dependents 2 scripts 229 downloadsrobStepSplitReg - Robust Stepwise Split Regularized Regression
Functions to perform robust stepwise split regularized regression. The approach first uses a robust stepwise algorithm to split the variables into the models of an ensemble. An adaptive robust regularized estimator is then applied to each subset of predictors in the models of an ensemble.
Last updated 5 months ago
openblascpp
3.18 score 1 stars 1 dependents 2 scripts 271 downloadssrlars - Split Robust Least Angle Regression
Functions to perform split robust least angle regression. The approach first uses the least angle regression algorithm to split the variables into the models of an ensemble and robust estimates of the correlation between predictors. An elastic net estimator is then applied to the selected predictors in each model using the imputed data from the detect deviating cell (DDC) method.
Last updated 5 months ago
3.18 score 1 stars 1 dependents 2 scripts 246 downloadsPSGD - Projected Subset Gradient Descent
Functions to generate ensembles of generalized linear models using a greedy projected subset gradient descent algorithm. The sparsity and diversity tuning parameters are selected by cross-validation.
Last updated 1 months ago
openblascppopenmp
3.00 score 2 scripts 323 downloadsstepSplitReg - Stepwise Split Regularized Regression
Functions to perform stepwise split regularized regression. The approach first uses a stepwise algorithm to split the variables into the models with a goodness of fit criterion, and then regularization is applied to each model. The weights of the models in the ensemble are determined based on a criterion selected by the user.
Last updated 1 months ago
openblascppopenmp
3.00 score 2 scripts 325 downloadsRMSS - Robust Multi-Model Subset Selection
Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation.
Last updated 1 months ago
openblascppopenmp
3.00 score 1 stars 4 scripts 297 downloadsSplitReg - Split Regularized Regression
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.
Last updated 5 months ago
cppopenmp
2.81 score 1 stars 13 scripts 117 downloadsCPGLIB - Competing Proximal Gradients Library
Functions to generate ensembles of generalized linear models using competing proximal gradients. The optimal sparsity and diversity tuning parameters are selected via an alternating grid search.
Last updated 5 months ago
openblascppopenmp
2.70 score 2 scripts 293 downloadssplitSelect - Best Split Selection Modeling for Low-Dimensional Data
Functions to generate or sample from all possible splits of features or variables into a number of specified groups. Also computes the best split selection estimator (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.
Last updated 5 months ago
2.70 score 4 scripts 156 downloadsnnGarrote - Non-Negative Garrote Estimation with Penalized Initial Estimators
Functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.
Last updated 5 months ago
2.70 score 1 stars 6 scripts 237 downloadssimTargetCov - Data Transformation or Simulation with Empirical Covariance Matrix
Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.
Last updated 5 months ago
2.70 score 2 scripts 191 downloads