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R: rpart.LAD: Least Absolute Deviation Regression Trees
...rpart.LAD: Least Absolute Deviation Regression Trees Description Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by Breiman, Friedman, Olshen...
/CRAN/refmans/rpart.LAD/html/rpart.LAD-package.html
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R: Least Absolute Deviation Forecast Combination
comb_LAD {ForecastComb} R Documentation Least Absolute Deviation Forecast Combination Description Computes forecast combination weights using least absolute deviation (LAD) regression. Usage comb_LAD(x)...
/CRAN/refmans/ForecastComb/html/comb_LAD.html
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R: Least Absolute Deviation Forecast Combination
comb_LAD {GeomComb} R Documentation Least Absolute Deviation Forecast Combination Description Computes forecast combination weights using least absolute deviation (LAD) regression. Usage comb_LAD(x)...
/CRAN/refmans/GeomComb/html/comb_LAD.html
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R: Least Absolute Deviation Regression Trees
Least Absolute Deviation Regression Trees Documentation for package ‘rpart.LAD’ version 0.1.3 DESCRIPTION file. Help Pages LAD 'rpart'-method: List of required functions for inducing 'rpart'-like...
/CRAN/refmans/rpart.LAD/html/00Index.html
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R: Fitter functions for least absolute deviation (LAD)...
...least absolute deviation (LAD) regression Description This function is a switcher among various numerical fitting functions (lad.fit.BR, and lad.fit.EM). The argument method does the switching: "BR"...
/CRAN/refmans/L1pack/html/lad.fit.html
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R: Fit a least absolute deviation (LAD) regression model
...least absolute deviation (LAD) regression model Description Fits a linear model using LAD methods, returning the bare minimum computations. Usage lad.fit.BR(x, y, tol = 1e-7) lad.fit.EM(x, y, tol = 1e-...
/CRAN/refmans/L1pack/html/lad.fit-methods.html
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R: Least Absolute Deviation Fit of Ultrametrics to...
l1_fit_ultrametric {clue} R Documentation Least Absolute Deviation Fit of Ultrametrics to Dissimilarities Description Find the ultrametric with minimal absolute distance (Manhattan dissimilarity)...
/CRAN/refmans/clue/html/l1_fit_ultrametric.html
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R: Gradient tree boosting with least absolute deviation (LAD)...
...tree boosting with least absolute deviation (LAD) loss Description A poor-man's implementation of stochastic gradient tree boosting with LAD loss. Print basic information about a fitted "ladboost"...
/CRAN/refmans/treemisc/html/ladboost.html
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R: Routines for L1 Estimation
...deviations regression lad.fit Fitter functions for least absolute deviation (LAD) regression lad.fit.BR Fit a least absolute deviation (LAD) regression model lad.fit.EM Fit a least absolute deviation ...
/CRAN/refmans/L1pack/html/00Index.html
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R: Data Sets and Functions to Accompany "Tree-Based Methods for Statistical Learning in R"
...banknote2 Swiss banknote data (UCI version) calibrate External probability calibration cummean Cumulative means decision_boundary Add decision boundary to a scatterplot. decision_boundary.default Add ...
/CRAN/refmans/treemisc/html/00Index.html
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R: L1 Linear Regression
...system A x = b in an Lp sense, that is minimize the term sum |b - A x|^p. The case p=1 is also called “least absolute deviation” (LAD) regression. Usage L1linreg(A, b, p = 1, tol = 1e-07, maxiter...
/CRAN/refmans/pracma/html/l1linreg.html
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R: (Geometric) Forecast Combination Methods
...comb_BG Bates/Granger (1969) Forecast Combination Approach comb_CLS Constrained Least Squares Forecast Combination comb_EIG1 Standard Eigenvector Forecast Combination comb_EIG2 Bias-Corrected...
/CRAN/refmans/GeomComb/html/00Index.html
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R: Forecast Combination Methods
...comb_BG Bates/Granger (1969) Forecast Combination Approach comb_CLS Constrained Least Squares Forecast Combination comb_CSR All Possible Combinations Forecast Averaging comb_EIG1 Standard Eigenvector...
/CRAN/refmans/ForecastComb/html/00Index.html
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R: Two-phase half-life estimation by linear fitting
...initial and terminal half-life by two-phase linear regression fitting. Usage lee(conc, time, points=3, method=c("ols", "lad", "hub", "npr"), lt=TRUE) Arguments conc Levels of concentrations. time Time...
/CRAN/refmans/PK/html/lee.html
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R: Cluster Ensembles
...cl_hard_partition Partitions as.cl_hierarchy Hierarchies as.cl_membership Memberships of Partitions as.cl_partition Partitions as.cl_ultrametric Ultrametrics of Hierarchies Cassini Cassini Data CKME...
/CRAN/refmans/clue/html/00Index.html
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R: Train RGBM predictor
...RGBM.train(X.train, Y.train, s_f = 0.3, s_s = 1, lf = 1, M.train = 5000, nu = 0.001) Arguments X.train Input S-by-P feature matrix of training samples. Columns correspond to features, rows correspond...
/CRAN/refmans/RGBM/html/RGBM.train.html
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R: Train GBM predictor
...can be used to predict response for unseen data with GBM.test function. Usage GBM.train(X.train, Y.train, s_f = 0.3, s_s = 1, lf =1, M.train = 5000, nu = 0.001) Arguments X.train Input N-by-p feature...
/CRAN/refmans/RGBM/html/GBM.train.html
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R: EM-like Algorithm for Semiparametric Mixtures of Regressions
...Hunter and Young (2012). Usage spregmix(lmformula, bw = NULL, constbw = FALSE, bwmult = 0.9, z.hat = NULL, symm = TRUE, betamethod = "LS", m = ifelse(is.null(z.hat), 2, ncol(z.hat)), epsilon = 1e-04...
/CRAN/refmans/mixtools/html/spregmix.html
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R: Test the case weighted regression estimator by Empirical...
...the case weighted estimation method. The log empirical likelihood been maximized is \sum_{d=1} \log \Delta F(y_i) + \sum_{d=0} \log [1-F(y_i)]. Usage WRegTest(x, y, delta, beta0, psifun=function(t){t})...
/CRAN/refmans/emplik/html/WRegTest.html
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R: Data Sets and Functions to Accompany "Tree-Based Methods for...
...Miscellaneous data sets and functions to accompany Greenwell (2022) "Tree-Based Methods for Statistical Learning in R" <doi:10.1201/9781003089032>. Details The DESCRIPTION file: Package: treemisc Type...
/CRAN/refmans/treemisc/html/treemisc-package.html
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R: Calculate Gene Regulatory Network from Expression data using...
GBM {RGBM} R Documentation Calculate Gene Regulatory Network from Expression data using either LS-TreeBoost or LAD-TreeBoost Description This function calculates a Ntfs-by-Ntargets adjacency matrix...
/CRAN/refmans/RGBM/html/GBM.html
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R: Re-iterate through the core GBM model building with optimal...
...factors obtained from select_ideal_k followed by get_colids for individual target gene to return a regularized GRN. Usage second_GBM_step(E, K, df_colids, tfs, targets, Ntfs, Ntargets, lf, M, nu, s_f)...
/CRAN/refmans/RGBM/html/second_GBM_step.html
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R: Perform the regularized GBM modelling once the initial GRN is...
...gene followed by re-iterating through the core GBM model and the refinement step to produce the final reverse engineered GRN. Usage regularized_GBM_step(E, A_prev, K, tfs, targets, Ntfs, Ntargets, lf...
/CRAN/refmans/RGBM/html/regularized_GBM_step.html
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R: Perform either LS-Boost or LAD-Boost ('GBM') on expression...
first_GBM_step {RGBM} R Documentation Perform either LS-Boost or LAD-Boost (GBM) on expression matrix E followed by the null_model_refinement_step Description This function utilizes the core gradient...
/CRAN/refmans/RGBM/html/first_GBM_step.html
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R: Multiple Testing Adjustment of p-values for S3 objects...
p_adjust {hdm} R Documentation Multiple Testing Adjustment of p-values for S3 objects rlassoEffects and lm Description Multiple hypotheses testing adjustment of p-values from a high-dimensional linear...
/CRAN/refmans/hdm/html/p_adjust.html
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R: Regularized Gradient Boosting Machine for inferring GRN
...form of a mechanistic network g_M and after generation of an initially inferred GRN using the core GBM model undergoes a pruning step. Here we detect and remove isolated nodes using the select_ideal_k...
/CRAN/refmans/RGBM/html/RGBM.html
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R: Gradient Boosting Regressor
...Boosting Regressor Usage h2o4gpu.gradient_boosting_regressor(loss = "ls", learning_rate = 0.1, n_estimators = 100L, subsample = 1, criterion = "friedman_mse", min_samples_split = 2L, min_samples_leaf...
/CRAN/refmans/h2o4gpu/html/h2o4gpu.gradient_boosting_regressor.html
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R: Periodogram based on (robustly) fitting a periodic function...
...accuracies. See RobPer-package for more information about light curves. For a lot of more details see Thieler, Fried and Rathjens (2016) and Thieler et al. (2013). Usage RobPer(ts, weighting, periods...
/CRAN/refmans/RobPer/html/RobPer.html
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R: roben: Robust Bayesian Variable Selection for...
...implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly...
/CRAN/refmans/roben/html/roben-package.html
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R: k-folds cross-validation for regnet
...(s) of lambda. Usage cv.regnet( X, Y, response = c("binary", "continuous", "survival"), penalty = c("network", "mcp", "lasso"), lamb.1 = NULL, lamb.2 = NULL, folds = 5, r = NULL, clv = NULL, initiation...
/CRAN/refmans/regnet/html/cv.regnet.html
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R: CivisML Gradient Boosting Regressor
...dependent_variable, primary_key = NULL, excluded_columns = NULL, loss = c("ls", "lad", "huber", "quantile"), learning_rate = 0.1, n_estimators = 500, subsample = 1, criterion = c("friedman_mse", "mse"...
/CRAN/refmans/civis/html/civis_ml_gradient_boosting_regressor.html
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