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algorithm.\n\n```r\nrcpp_ksNN(Label, Distance, L_C = 1)\n```\n\n## Arguments\n\n- `Label`: vectors of the known labels of the samples.\n- `Distance`: vectors of the distance between the target sample we want to predict and the other samples.\n- `L_C`: parameter of k* nearest neighbors algorithm.\n\n## Returns\n\nthe prediction value(pred) and the weight of the samples(alpha).\n\n\n\n## Note\n\nThis algorithm is based on Anava and Levy(2017).\n\n## Examples\n\n```r\nlibrary(ksNN)\n set.seed(1)\n\n #make the nonlinear regression problem\n X<-runif(100)\n Y<-X^6-3*X^3+5*X^2+2\n\n suffle<-order(rnorm(length(X)))\n X<-X[suffle]\n Y<-Y[suffle]\n\n test_X<-X[1]\n test_Y<-Y[1]\n\n train_X<-X[-1]\n train_Y<-Y[-1]\n\n Label<-train_Y\n Distance<-sqrt((test_X-train_X)^2)\n\n pred_ksNN<-rcpp_ksNN(Label,Distance,L_C=1)\n\n #the predicted value with k*NN\n pred_ksNN$pred\n\n #the 'true' value\n test_Y\n```"}]}]}]}]}]}],["$","div",null,{"className":"lg:w-1/3 lg:border-l px-1 sm:px-8 pt-8 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