Showing 8 of total 8 results (show query)
bdwilliamson
vimp:Perform Inference on Algorithm-Agnostic Variable Importance
Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
Maintained by Brian D. Williamson. Last updated 2 months ago.
machine-learningnonparametric-statisticsstatistical-inferencevariable-importance
23 stars 6.79 score 67 scripts
philboileau
cvCovEst:Cross-Validated Covariance Matrix Estimation
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.
Maintained by Philippe Boileau. Last updated 1 years ago.
covariance-matrix-estimationcross-validationhigh-dimensional-statisticsnonparametric-statistics
13 stars 6.78 score 26 scripts 2 dependents
nt-williams
lmtp:Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies
Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).
Maintained by Nicholas Williams. Last updated 26 days ago.
causal-inferencecensored-datalongitudinal-datamachine-learningmodified-treatment-policynonparametric-statisticsprecision-medicinerobust-statisticsstatisticsstochastic-interventionssurvival-analysistargeted-learning
64 stars 6.37 score 91 scripts
noramvillanueva
clustcurv:Determining Groups in Multiples Curves
A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Maintained by Nora M. Villanueva. Last updated 5 months ago.
clusteringdata-analyticsmachinelearningmultiple-curvesnonparametric-statisticsnumber-of-clustersregressionsurvival-analysis
3 stars 5.53 score 38 scripts
qddyy
LearnNonparam:'R6'-Based Flexible Framework for Permutation Tests
Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests.
Maintained by Yan Du. Last updated 2 months ago.
hypothesis-testnonparametric-statisticspermutation-testcpp
6 stars 4.95 score 2 scripts
egarpor
DirStats:Nonparametric Methods for Directional Data
Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in García-Portugués (2013) <doi:10.1214/13-ejs821>.
Maintained by Eduardo García-Portugués. Last updated 2 years ago.
directional-statisticsnonparametric-statisticsstatisticsfortran
12 stars 4.26 score 7 scripts 1 dependents
happma
pseudorank:Pseudo-Ranks
Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. See Happ et al. (2020, <doi:10.18637/jss.v095.c01>) for details.
Maintained by Martin Happ. Last updated 1 months ago.
cppnonparametricnonparametric-statisticspseudo-rankpseudo-ranksrankrank-teststrend-testcpp
3 stars 3.71 score 17 scriptsnoramvillanueva
seq2R:Simple Method to Detect Compositional Changes in Genomic Sequences
This software is useful for loading '.fasta' or '.gbk' files, and for retrieving sequences from 'GenBank' dataset <https://www.ncbi.nlm.nih.gov/genbank/>. This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in 'seq2R'.
Maintained by Nora M. Villanueva. Last updated 4 months ago.
bootstrapchange-pointsdna-sequencesgenome-analysismachine-learningnonparametric-statisticsregressionfortran
3.00 score 10 scripts