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{ getIn, is } from './helpers'\n\nconst { abs, exp, log, pow, sqrt, PI } = Math\nconst SQ2PI = sqrt(2 * PI) // eslint-disable-line no-unused-vars\nconst EPS = 1e-8\nconst fpmin = 1e-30\nconst visibleProbability = [200, 100, 50, 20, 10, 1]\nconst zScore = {\n  200: 1.282,\n  100: 1.645,\n  50: 1.96,\n  20: 2.33,\n  10: 2.576,\n  1: 3.291\n}\n\nconst constants = {\n  visibleProbability,\n  confidenceLevels: visibleProbability.map((_, i) => 1 - visibleProbability[i] / 1000),\n  zScore\n}\n\n//normal-distribution (-\\infty, z]\nconst normaldist = to => {\n  const z = to / sqrt(2)\n  const t = 1 / (1 + 0.3275911 * abs(z))\n  const a1 = 0.254829592\n  const a2 = -0.284496736\n  const a3 = 1.421413741\n  const a4 = -1.453152027\n  const a5 = 1.061405429\n  const erf = 1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * exp(-z * z)\n  const sign = z < 0 ? -1 : 1\n  return (1 / 2) * (1 + sign * erf)\n}\n\n/*\ntwo-sample z-test for difference in proportion;\n'N-1' correction applied (Doi: 10.1002/sim.2832)\nSmall sample if n1<30 or n2<30 or chi-square expected values <1\nconf is expressed in 1/1000s, so 50 means .05\n*/\nconst pValueByZ = z => 2 * (1 - normaldist(abs(z)))\n\nconst sigTest = (x1, x2, n1, n2, conf = 50) => {\n  const p1 = x1 / n1\n  const p2 = x2 / n2\n  const n = n1 + n2\n  const chiSquareCellExpectedValue1_1 = ((x1 + x2) * n1) / n\n  const chiSquareCellExpectedValue1_2 = ((x1 + x2) * n2) / n\n  const chiSquareCellExpectedValue2_1 = ((n - x1 - x2) * n1) / n\n  const chiSquareCellExpectedValue2_2 = ((n - x1 - x2) * n2) / n\n  if (\n    n < 1 ||\n    n1 < 30 ||\n    n2 < 30 ||\n    chiSquareCellExpectedValue1_1 < 1 ||\n    chiSquareCellExpectedValue1_2 < 1 ||\n    chiSquareCellExpectedValue2_1 < 1 ||\n    chiSquareCellExpectedValue2_2 < 1\n  ) {\n    // small sample case\n    return false\n  }\n  const pooledP = (x1 + x2) / (n1 + n2)\n  const se = sqrt(pooledP * (1 - pooledP) * (1 / n1 + 1 / n2))\n  const z = (sqrt((n - 1) / n) * (p1 - p2)) / se\n  const pValue = pValueByZ(z)\n  return pValue < conf / 1000\n}\n\nconst gammaln = x => {\n  let j = 0\n  const cof = [\n    76.18009172947146, -86.50532032941678, 24.01409824083091, -1.231739572450155, 0.1208650973866179e-2,\n    -0.5395239384953e-5\n  ]\n  let ser = 1.000000000190015\n  let xx\n  let y\n  let tmp = (y = xx = x) + 5.5\n  tmp -= (xx + 0.5) * log(tmp)\n  for (; j < 6; j++) ser += cof[j] / ++y\n  return log((2.5066282746310007 * ser) / xx) - tmp\n}\n\nconst betacf = (x, a, b) => {\n  let m = 1\n  const qab = a + b\n  const qap = a + 1\n  const qam = a - 1\n  let c = 1\n  let d = 1 - (qab * x) / qap\n  let m2\n  let aa\n  let del\n  let h\n\n  // These q's will be used in factors that occur in the coefficients\n  if (abs(d) < fpmin) d = fpmin\n  d = 1 / d\n  h = d\n\n  for (; m <= 100; m++) {\n    m2 = 2 * m\n    aa = (m * (b - m) * x) / ((qam + m2) * (a + m2))\n    // One step (the even one) of the recurrence\n    d = 1 + aa * d\n    if (abs(d) < fpmin) d = fpmin\n    c = 1 + aa / c\n    if (abs(c) < fpmin) c = fpmin\n    d = 1 / d\n    h *= d * c\n    aa = (-(a + m) * (qab + m) * x) / ((a + m2) * (qap + m2))\n    // Next step of the recurrence (the odd one)\n    d = 1 + aa * d\n    if (abs(d) < fpmin) d = fpmin\n    c = 1 + aa / c\n    if (abs(c) < fpmin) c = fpmin\n    d = 1 / d\n    del = d * c\n    h *= del\n    if (abs(del - 1) < 3e-7) break\n  }\n\n  return h\n}\n\nconst ibeta = (x, a, b) => {\n  // Returns the incomplete beta function I_x(a,b)\n  // Factors in front of the continued fraction.\n  const bt = 0 === x || 1 === x ? 0 : exp(gammaln(a + b) - gammaln(a) - gammaln(b) + a * log(x) + b * log(1 - x))\n  if (0 > x || 1 < x) return false\n  if (x < (a + 1) / (a + b + 2))\n    // Use continued fraction directly.\n    return (bt * betacf(x, a, b)) / a\n  // else use continued fraction after making the symmetry transformation.\n  return 1 - (bt * betacf(1 - x, b, a)) / b\n}\n\nconst ibetainv = (p, a, b) => {\n  // Returns the inverse of the incomplete beta function\n  const a1 = a - 1\n  const b1 = b - 1\n  let j = 0\n  let lna\n  let lnb\n  let pp\n  let t\n  let u\n  let err\n  let x\n  let al\n  let h\n  let w\n  if (p <= 0) return 0\n  if (p >= 1) return 1\n  if (a >= 1 && b >= 1) {\n    pp = p < 0.5 ? p : 1 - p\n    t = sqrt(-2 * log(pp))\n    x = (2.30753 + t * 0.27061) / (1 + t * (0.99229 + t * 0.04481)) - t\n    if (p < 0.5) x = -x\n    al = (x * x - 3) / 6\n    h = 2 / (1 / (2 * a - 1) + 1 / (2 * b - 1))\n    w = (x * sqrt(al + h)) / h - (1 / (2 * b - 1) - 1 / (2 * a - 1)) * (al + 5 / 6 - 2 / (3 * h))\n    x = a / (a + b * exp(2 * w))\n  } else {\n    lna = log(a / (a + b))\n    lnb = log(b / (a + b))\n    t = exp(a * lna) / a\n    u = exp(b * lnb) / b\n    w = t + u\n    x = p < t / w ? pow(a * w * p, 1 / a) : 1 - pow(b * w * (1 - p), 1 / b)\n  }\n  const afac = -gammaln(a) - gammaln(b) + gammaln(a + b)\n  for (; j < 10; j++) {\n    if (0 === x || 1 === x) return x\n    err = ibeta(x, a, b) - p\n    t = exp(a1 * log(x) + b1 * log(1 - x) + afac)\n    u = err / t\n    x -= t = u / (1 - 0.5 * Math.min(1, u * (a1 / x - b1 / (1 - x))))\n    if (x <= 0) x = 0.5 * (x + t)\n    if (x >= 1) x = 0.5 * (x + t + 1)\n    if (abs(t) < EPS * x && j > 0) break\n  }\n  return x\n}\nconst qbeta = ibetainv\n\nconst Betinc = (X, A, B) => {\n  let A0 = 0\n  let B0 = 1\n  let A1 = 1\n  let B1 = 1\n  let M9 = 0\n  let A2 = 0\n  let C9\n  while (abs((A1 - A2) / A1) > 0.00001) {\n    A2 = A1\n    C9 = (-(A + M9) * (A + B + M9) * X) / (A + 2 * M9) / (A + 2 * M9 + 1)\n    A0 = A1 + C9 * A0\n    B0 = B1 + C9 * B0\n    M9 = M9 + 1\n    C9 = (M9 * (B - M9) * X) / (A + 2 * M9 - 1) / (A + 2 * M9)\n    A1 = A0 + C9 * A1\n    B1 = B0 + C9 * B1\n    A0 = A0 / B1\n    B0 = B0 / B1\n    A1 = A1 / B1\n    B1 = 1\n  }\n  return A1 / A\n}\n\nconst tcdf = (X, df) => {\n  if (0 >= df) {\n    throw new Error('Degrees of freedom must be positive')\n  }\n  const A = df / 2\n  const S = A + 0.5\n  const Z = df / (df + X * X)\n  const BT = exp(gammaln(S) - gammaln(0.5) - gammaln(A) + A * log(Z) + 0.5 * log(1 - Z))\n  const betacdf = Z < (A + 1) / (S + 2) ? BT * Betinc(Z, A, 0.5) : 1 - BT * Betinc(1 - Z, 0.5, A)\n  let res = X < 0 ? betacdf / 2 : 1 - betacdf / 2\n  res = Math.round(res * 100000) / 100000\n  return res\n}\n\nconst tScoreCritical = (significanceLevel, df) => {\n  // come up with t that is on edge of significance\n  let tLeft\n  let tRight\n  let pvalLeft\n  let pvalRight\n  let t = 500\n  let epsilon = 100\n  if (df > 10) {\n    t = 2\n    epsilon = 2\n  }\n  for (let i = 0; i < 1000; i++) {\n    tLeft = t - epsilon\n    if (tLeft < 0.0001) {\n      tLeft = 0.0001\n    }\n    tRight = t + epsilon\n    if (tRight < 0.001) {\n      tRight = 0.001\n    }\n    pvalLeft = 2 * (1 - tcdf(tLeft, df))\n    pvalRight = 2 * (1 - tcdf(tRight, df))\n    if (pvalLeft > significanceLevel && pvalRight > significanceLevel) {\n      t += epsilon\n    }\n    if (pvalLeft <= significanceLevel && pvalRight <= significanceLevel) {\n      t -= epsilon\n    }\n    if (pvalLeft > significanceLevel && pvalRight <= significanceLevel) {\n      epsilon = epsilon / 5\n    }\n    if (epsilon < 0.0001) {\n      break\n    }\n  }\n  return t\n}\n\n// eslint-disable-next-line no-unused-vars\nconst sigTestGetErrorMeanTwoSample = (averageN, averageSd, confidenceLevel = 0.95) => {\n  const significanceLevel = 1 - (isNaN(Number(confidenceLevel)) ? 0.95 : Number(confidenceLevel))\n  const se = sqrt(pow(averageSd, 2) / averageN + pow(averageSd, 2) / averageN)\n  const df = Math.round(\n    pow(se, 4) /\n      (pow(pow(averageSd, 2) / averageN, 2) / (averageN - 1) + pow(pow(averageSd, 2) / averageN, 2) / (averageN - 1))\n  )\n  const tScore = tScoreCritical(significanceLevel, df)\n  const marginOfError = tScore * se\n  return marginOfError\n}\n\nconst sigTestGetErrorMeanOneSample = (n, sd, confidenceLevel = 0.95) => {\n  const significanceLevel = 1 - (isNaN(Number(confidenceLevel)) ? 0.95 : Number(confidenceLevel))\n  const se = sd / sqrt(n)\n  const df = Math.round(n) - 1\n  const tScore = tScoreCritical(significanceLevel, df)\n  const marginOfError = tScore * se\n  return marginOfError\n}\n\n// eslint-disable-next-line max-params\nconst sigTestMeanTwoSample = (mu1, mu2, sd1, sd2, n1, n2, confidenceLevel = 0.95) => {\n  //small sample case:\n  if (n1 < 30 || n2 < 30) {\n    return false\n  }\n  const se = sqrt(pow(sd1, 2) / n1 + pow(sd2, 2) / n2)\n  const df = Math.round(pow(se, 4) / (pow(pow(sd1, 2) / n1, 2) / (n1 - 1) + pow(pow(sd2, 2) / n2, 2) / (n2 - 1)))\n  const t = (mu1 - mu2) / se\n\n  const pValue = 2 * (1 - tcdf(abs(t), df))\n  const pValLimit = 1 - (isNaN(Number(confidenceLevel)) ? 0.95 : Number(confidenceLevel))\n  return pValue < pValLimit\n}\n\n/*\nerror bars (two-sided test)  (blue range):\nqbeta=beta quantile function similar to R's qbeta. R\nOnly works with count data (so not scores, not distribute summary, not reorder summary, etc)\n*/\nconst calcErrorForNode = (amount, total, conf = 50) => {\n  const conf_level = (1000 - conf) / 1000\n  const errLO = amount - total * qbeta((1 - conf_level) / 2, 0.5 + amount, 0.5 + total - amount)\n  const errHI = total * qbeta(1 - (1 - conf_level) / 2, 0.5 + amount, 0.5 + total - amount) - amount\n  return [Math.max(0, errLO) || 0, Math.max(0, errHI) || 0]\n}\n\nconst calcNodeError = node => {\n  if (null === node || Array.isArray(node) || !is(Object)(node)) return\n\n  const zScoreKeys = Object.keys(zScore)\n  Object.keys(node).forEach(key => {\n    if (!node[key]) return\n\n    if (Object.hasOwn(node, 'total') && Object.hasOwn(node[key], 'amount')) {\n      const total = node[key].base || node.base || node.total\n      if (total < node[key].amount) return\n      // const effectiveSize = node.effSize ? node.effSize : total\n\n      for (const zScoreKey of zScoreKeys) {\n        const err = calcErrorForNode(node[key].amount, total, zScoreKey)\n        node[key].errLO = Object.assign(node[key].errLO || {}, {\n          [String(1000 - zScoreKey)]: { amount: Number(err[0]), percents: Number((100 * err[0]) / total) }\n        })\n        node[key].errHI = Object.assign(node[key].errHI || {}, {\n          [String(1000 - zScoreKey)]: { amount: Number(err[1]), percents: Number((100 * err[1]) / total) }\n        })\n        if (node.respTotal) {\n          const respErr = calcErrorForNode(node[key].amount, node.respTotal, zScoreKey)\n          node[key].errLO[String(1000 - zScoreKey)].respAmount = Number(respErr[0])\n          node[key].errLO[String(1000 - zScoreKey)].respPercents = Number((100 * respErr[0]) / node.respTotal)\n          node[key].errHI[String(1000 - zScoreKey)].respAmount = Number(respErr[1])\n          node[key].errHI[String(1000 - zScoreKey)].respPercents = Number((100 * respErr[1]) / node.respTotal)\n        }\n      }\n    }\n\n    if (Object.hasOwn(node[key], 'total')) {\n      calcNodeError(node[key])\n    }\n  })\n}\n\n/*\nСигтест максдиффа.\nOnly *utility scores*.\nmarginOfError = sf.sigTestGetErrorMeanOneSample(numberOfRespondentsSelected, mean(sd), confidenceLevel)\nгде sd = массив standard deviation, по элементу на каждый айтем, считается методом sf.sd\nтам есть зависимый метод tScoreCritical который не оптимизирован, если есть желание можно улучшить, заодно и для smart templates\nmarginOfError используется для несимметричного ренджа (band around the column top)\nсравнение колонок делается sf.sigTestMeanTwoSample(mu1, mu2, n1, n2, sd1, sd2, confidenceLevel = 0.95)\nгде mu1 mu2 - средние utility scores\n*/\nconst calcMaxdiffNodeError = node => {\n  // console.log('calcMaxdiffNodeError', node)\n  const zScoreKeys = Object.keys(zScore)\n  if (Object.hasOwn(node, 'total')) {\n    const elements = Object.keys(node).filter(k => /^b\\d+/.test(k))\n    // console.log('calcMaxdiffNodeError', elements)\n    // const mu = elements.map(b => node[b].rawZC)\n    // const probs = elements.map(b => node[b].probability / 100)\n    for (const zScoreKey of zScoreKeys) {\n      const confLevel = (1000 - zScoreKey) / 1000\n      elements.forEach(key => {\n        if (Object.hasOwn(node[key], 'sd')) {\n          const total = node[key].base || node.base || node.total\n          if (total <= 1) return\n\n          try {\n            const error = sigTestGetErrorMeanOneSample(total, node[key].sd, confLevel)\n            node[key].errLO = Object.assign(node[key].errLO || {}, {\n              [String(1000 - zScoreKey)]: {\n                rawAmount: Number(error),\n                probAmount: Number(\n                  100 *\n                    (exp(node[key].raw) / (1 + exp(node[key].raw)) -\n                      exp(node[key].raw - error) / (1 + exp(node[key].raw - error)))\n                )\n              }\n            })\n            node[key].errHI = Object.assign(node[key].errHI || {}, {\n              [String(1000 - zScoreKey)]: {\n                rawAmount: Number(error),\n                probAmount: Number(\n                  100 *\n                    (exp(node[key].raw + error) / (1 + exp(node[key].raw + error)) -\n                      exp(node[key].raw) / (1 + exp(node[key].raw)))\n                )\n              }\n            })\n          } catch {\n            delete node[key].errLO\n            delete node[key].errHI\n          }\n        }\n      })\n    }\n    // console.log('calcMaxdiffNodeError', node)\n  }\n}\n\nconst baseQuestionTypes = ['rb', 'cb']\nconst countableQuestionTypes = ['rb', 'cb', 'mx', 'mp', 'sr', 'se', 'ro', 'aro', 'cf', 'sh', 'sft']\nconst calcErrorsForNode = (type, node) => {\n  if (countableQuestionTypes.includes(type)) {\n    if (baseQuestionTypes.includes(type)) {\n      const keys = Object.keys(node).filter(key => null != node[key] && Object.hasOwn(node[key], 'percents'))\n      if (!keys.some(key => node[key]['percents'] > 100)) {\n        calcNodeError(node)\n      }\n    } else {\n      calcNodeError(node)\n    }\n  } else if ('md' === type) {\n    calcMaxdiffNodeError(node)\n  }\n}\nconst calcErrors = (questions = [], stats = {}) => {\n  for (const [id, answer] of Object.entries(stats)) {\n    const question = questions.find(q => ((0 < q.position && `q${q.position}`) || q.id) == id)\n    if (!question || !answer) continue\n\n    if (Object.hasOwn(question, 'blocks') && null != question.blocks) {\n      question.blocks.forEach(b => {\n        if (b.format != undefined && true == b.format.percentsOnly) return\n        calcErrorsForNode(b.variant || b.subtype, b.path && b.path.length ? getIn(b.path, answer) : answer)\n      })\n    } else {\n      const type = question.variant || question.type\n      calcErrorsForNode(type, answer)\n    }\n  }\n}\n\nconst calcNodeStatData = node => {\n  Object.keys(node).forEach(key => {\n    if (null == node[key] || 'object' != typeof node[key] || null == key.match(/^a\\d+$/i)) return\n    node[key].amount = node[key].amount || 0\n    node[key].percents = node[key].percents || 0\n  })\n\n  const respTotal = Object.keys(node).reduce((memo, key) => {\n    if (null == node[key] || 'object' != typeof node[key] || null == key.match(/^a\\d+$/i)) return memo\n    return memo + (node[key].amount || 0)\n  }, 0)\n  node.respTotal = respTotal\n\n  Object.keys(node).forEach(key => {\n    if (null == node[key] || 'object' != typeof node[key] || null == key.match(/^a\\d+$/i)) return\n    node[key].respPercents = parseFloat(((100 * node[key].amount) / respTotal || 0).toFixed(2))\n  })\n}\n\nconst calcNodeStatDataByType = (type, subtype, answer = {}) => {\n  if (['rb', 'cb', 'oex', 'sft'].includes(type)) {\n    calcNodeStatData(answer)\n  }\n  if ('mp' == type && 'default' == subtype) {\n    Object.keys(answer).forEach(key => {\n      if (null == answer[key] || 'object' != typeof answer[key] || null == key.match(/^b\\d+$/i)) return\n      calcNodeStatData(answer[key])\n    })\n  }\n}\n\nconst calcStatData = (questions = [], stats = {}) => {\n  for (const [id, answer] of Object.entries(stats)) {\n    const question = questions.find(q => ((0 < q.position && `q${q.position}`) || q.id) == id)\n    if (!question || !answer) continue\n\n    if (Object.hasOwn(question, 'blocks') && null !== question.blocks) {\n      question.blocks.forEach(b =>\n        calcNodeStatDataByType(b.variant || b.subtype, b.subType, b.path ? getIn(b.path, answer) : answer)\n      )\n    } else {\n      const type = question.variant || question.type\n      calcNodeStatDataByType(type, question.subType, answer)\n    }\n  }\n  calcErrors(questions, stats)\n}\n\nconst findNeighbors = (sorted_array, included_indexes, target_idx, threshold) => {\n  const target_point = sorted_array[target_idx]\n  let neighbors = []\n  // below search\n  const target_included_idx = included_indexes.indexOf(target_idx)\n  for (let _i = target_included_idx; _i >= 0; _i--) {\n    const distance = sorted_array[included_indexes[_i]].x - target_point.x\n    if (distance > threshold) {\n      break\n    } else {\n      neighbors.push(included_indexes[_i])\n    }\n  }\n  neighbors = neighbors.reverse()\n  // above search\n  for (let _i = target_included_idx + 1; _i < included_indexes.length; _i++) {\n    if (sorted_array[included_indexes[_i]].x - target_point.x > threshold) {\n      break\n    } else {\n      neighbors.push(included_indexes[_i])\n    }\n  }\n  // list of neighbor indexes\n  return neighbors\n}\n\nconst findNeighborsXY = (sorted_x, included_indexes, target_point_index, threshold) => {\n  const target_point = sorted_x[target_point_index]\n\n  // find target point in sorted arrays\n  const x_neighbors_idxs = findNeighbors(sorted_x, included_indexes, target_point_index, threshold)\n\n  const real_neighbors = x_neighbors_idxs.filter(\n    nb_idx =>\n      Math.sqrt(Math.pow(sorted_x[nb_idx].x - target_point.x, 2) + Math.pow(sorted_x[nb_idx].y - target_point.y, 2)) <=\n      threshold\n  )\n\n  return real_neighbors\n}\n\nconst findGroup = (sorted_array, included_indexes, threshold) => {\n  let group_idxs = [] // resulting array of indexes\n  let neighbors = [included_indexes[0]] // intermediate array of indexes\n  let new_neighbors = [included_indexes[0]]\n\n  let remaining_indexes = included_indexes.slice()\n  while (neighbors.length > 0 && remaining_indexes.length > 0) {\n    new_neighbors = []\n    neighbors.forEach(target_idx => {\n      const last_found_neighbors = findNeighborsXY(sorted_array, remaining_indexes, target_idx, threshold)\n      new_neighbors = new_neighbors.concat(last_found_neighbors)\n      remaining_indexes = remaining_indexes.filter(el => !last_found_neighbors.includes(el))\n    })\n\n    group_idxs = group_idxs.concat(new_neighbors)\n    neighbors = new_neighbors\n  }\n  return group_idxs\n}\n\nconst calcPointsGroups = (points = [], threshold = 0) => {\n  const sortedByX = points.sort((a, b) => a.x - b.x)\n\n  let _indexes = sortedByX.map((_p, index) => index)\n  const point_groups = []\n\n  while (_indexes.length > 0) {\n    const neighbors_group = findGroup(sortedByX, _indexes, threshold)\n    point_groups.push(neighbors_group)\n    _indexes = _indexes.filter(idx => !neighbors_group.includes(idx))\n  }\n\n  const groups = point_groups.map(group => group.map(index => sortedByX[index]))\n  return groups\n}\n\nexport default {\n  calcStatData,\n  calcErrors,\n  calcErrorForNode,\n  calcPointsGroups,\n  constants,\n  normaldist,\n  qbeta,\n  tcdf,\n  gammaln,\n  tScoreCritical,\n  sigTest,\n  sigTestMeanTwoSample\n}\n"],"names":["abs","Math","exp","log","pow","sqrt","PI","fpmin","visibleProbability","zScore","constants","confidenceLevels","map","_","i","normaldist","to","z","t","gammaln","x","xx","y","j","cof","ser","tmp","betacf","a","b","m2","aa","del","h","m","qab","qap","qam","c","d","ibeta","bt","qbeta","p","lna","lnb","u","al","w","a1","b1","afac","min","Betinc","X","A","B","C9","A0","B0","A1","B1","M9","A2","tcdf","df","Error","S","Z","BT","betacdf","res","round","tScoreCritical","significanceLevel","tLeft","tRight","pvalLeft","pvalRight","epsilon","calcErrorForNode","amount","total","conf_level","arguments","length","undefined","errLO","errHI","max","calcNodeError","node","Array","isArray","is","Object","zScoreKeys","keys","forEach","key","hasOwn","base","_i2","_zScoreKeys","zScoreKey","err","assign","_defineProperty","String","Number","percents","respTotal","respErr","respAmount","respPercents","calcMaxdiffNodeError","_step","elements","filter","k","test","_iterator","_createForOfIteratorHelper","_loop","value","confLevel","error","n","sd","confidenceLevel","isNaN","se","sigTestGetErrorMeanOneSample","rawAmount","probAmount","raw","_unused","s","done","e","f","baseQuestionTypes","countableQuestionTypes","calcErrorsForNode","type","includes","some","calcErrors","_ref2","questions","stats","_loop2","_ref","_Object$entries","_i3","id","_slicedToArray","answer","question","find","q","position","blocks","format","percentsOnly","variant","subtype","path","getIn","entries","calcNodeStatData","match","reduce","memo","parseFloat","toFixed","calcNodeStatDataByType","findNeighborsXY","sorted_x","included_indexes","target_point_index","threshold","target_point","x_neighbors_idxs","sorted_array","target_idx","neighbors","target_included_idx","indexOf","_i","push","reverse","findNeighbors","nb_idx","calcStatData","_ref4","_loop3","_ref3","_Object$entries2","_i4","subType","calcPointsGroups","sortedByX","sort","_indexes","_p","index","point_groups","_loop4","neighbors_group","group_idxs","new_neighbors","remaining_indexes","slice","last_found_neighbors","concat","el","findGroup","idx","group","sigTest","x1","x2","n1","n2","conf","p1","p2","pooledP","pValue","pValueByZ","sigTestMeanTwoSample","mu1","mu2","sd1","sd2"],"sourceRoot":""}