Databutler Stats

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io.github.physics-star-cat/databutler-statsv1.0.0

Exact statistics & probability: distributions, hypothesis tests, CIs, Bayesian updates, regression.

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bayes_update Read-only

Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors. Priors are renormalised to sum to 1.

hypothesesarray, required

Tools (6)

bayes_update

Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors. Priors are renormalised to sum to 1.

read-only

Argument	Type	Description
hypotheses*	array

Input schema (JSON)

{
  "type": "object",
  "required": [
    "hypotheses"
  ],
  "properties": {
    "hypotheses": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "prior",
          "likelihood"
        ],
        "properties": {
          "name": {
            "type": "string"
          },
          "prior": {
            "type": "number"
          },
          "likelihood": {
            "type": "number"
          }
        }
      }
    }
  }
}

confidence_interval

Confidence interval for a mean (t-based; from data, or n/mean/sd) or a proportion (Wilson; successes/n). kind = mean | proportion; confidence default 0.95.

read-only

Argument	Type	Description
n	number
sd	number
data	array
kind	string
mean	number
successes	number
confidence	number

Input schema (JSON)

{
  "type": "object",
  "properties": {
    "n": {
      "type": "number"
    },
    "sd": {
      "type": "number"
    },
    "data": {
      "type": "array",
      "items": {
        "type": "number"
      }
    },
    "kind": {
      "enum": [
        "mean",
        "proportion"
      ],
      "type": "string"
    },
    "mean": {
      "type": "number"
    },
    "successes": {
      "type": "number"
    },
    "confidence": {
      "type": "number"
    }
  }
}

descriptive_stats

Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.

read-only

Argument	Type	Description
data*	array

Input schema (JSON)

{
  "type": "object",
  "required": [
    "data"
  ],
  "properties": {
    "data": {
      "type": "array",
      "items": {
        "type": "number"
      }
    }
  }
}

distribution

Evaluate a probability distribution (normal, t, chi2, binomial, poisson): pdf/pmf and cdf at a value, and/or the quantile at a probability, plus mean & variance. Params per dist: normal {mean,sd}, t {df}, chi2 {df}, binomial {n,p}, poisson {lambda}.

read-only

Argument	Type	Description
p	number	probability to get the quantile for (0-1)
at	number	value to evaluate pdf/pmf and cdf at
dist*	string
params	object

Input schema (JSON)

{
  "type": "object",
  "required": [
    "dist"
  ],
  "properties": {
    "p": {
      "type": "number",
      "description": "probability to get the quantile for (0-1)"
    },
    "at": {
      "type": "number",
      "description": "value to evaluate pdf/pmf and cdf at"
    },
    "dist": {
      "enum": [
        "normal",
        "t",
        "chi2",
        "binomial",
        "poisson"
      ],
      "type": "string"
    },
    "params": {
      "type": "object"
    }
  }
}

hypothesis_test

Run a significance test and get the statistic, p-value, and a plain-language interpretation with assumptions. test = one-sample-t {data, mu0}, two-sample-t {data1, data2}, one-proportion-z {successes, n, p0}, two-proportion-z {successes1,n1,successes2,n2}, chi2-gof {observed, expected?}, chi2-independence {table}. Optional tail: two-sided (default) | greater | less; alpha default 0.05.

read-only

Argument	Type	Description
n	number
n1	number
n2	number
p0	number
mu0	number
data	array
tail	string
test*	string
alpha	number
data1	array

linear_regression

Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.

read-only

Argument	Type	Description
x*	array
y*	array

Input schema (JSON)

{
  "type": "object",
  "required": [
    "x",
    "y"
  ],
  "properties": {
    "x": {
      "type": "array",
      "items": {
        "type": "number"
      }
    },
    "y": {
      "type": "array",
      "items": {
        "type": "number"
      }
    }
  }
}