twolodzko

library(ggplot2)
set.seed(123)

# simulated data
n <- 100
nx <- ny <- 500
x <- rbinom(n, nx, 0.22)
y <- rbinom(n, ny, 0.21)

twolodzko

data {
  int<lower=3> n;
  vector[n] y;
  int<lower=0> h;
}
parameters {
  real<lower=0, upper=1> alpha;
  real<lower=0, upper=1> beta;
  real<lower=0> sigma;
  vector[2] mu_init;

twolodzko

#include <Rcpp.h>
using namespace Rcpp;


// [[Rcpp::export]]
List cpp_rle2(
    const NumericVector& x,
    const double h = 0,
    const double eps = 1e-16
) {

twolodzko

import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin

from sklearn.ensemble import IsolationForest
from sklearn.covariance import EllipticEnvelope
from sklearn.svm import OneClassSVM


def mode(x):
    (values, counts) = np.unique(x, return_counts = True)

twolodzko

lst <- function(...) {
  dots <- list(...)
  call <- match.call()
  names(dots) <- sapply(substitute(...()), as.character)
  dots
}

funs <- lst(mode, typeof, class, is.atomic, is.array, is.vector, is.numeric, is.character, is.logical, is.matrix, is.data.frame, is.list)
tests <- lst(

twolodzko

library(extraDistr)

bins <- function(x, n = 512L) {
  x <- x[!is.na(x)]
  h <- diff(range(x))/(n-4)
  mids <- seq(min(x)-2*h, max(x)+2*h, length.out = n)
  breaks <- c(mids[1L] - h, mids + h)
  counts <- unname(tapply(x, cut(x, breaks), length,
                   default = 0L))

twolodzko

set.seed(123)

x <- rnorm(7)

dx <- density(x)
plot(dx)

f <- approxfun(dx, yleft = 0, yright = 0)

twolodzko

set.seed(123)

N <- 1e5
mu <- c(2,4,6)
sigma <- rep(0.5, 3)
lambda <- (1:3)/6

dat <- c(

twolodzko

ptmp <- par(mfrow=c(2,2), mar = c(3,3,3,3))


set.seed(123)
n <- 7
x <- rnorm(n, sd = 3)

K <- function(x) dnorm(x)

twolodzko

priors <- c(0, 0.001, 0.333, 0.5, 1)

s <- c(0, 1, 1, 5, 5)
f <- c(1, 1, 2, 1, 25)



dbeta2 <- function(x, prior1, prior2 = prior1) {