statcpp CLI Test Reference

Note: The shell examples in this document use bash syntax and run on macOS and Linux. On Windows, unit tests (cmake -DGTEST=true) are supported; E2E tests (run_e2e.sh) require bash (Git Bash or WSL).

Basic syntax:

statcpp <category> <command> [options] [file]
statcpp <shortcut> [options] [file]

When no file argument is provided, input is read from stdin. Use --row to treat horizontal data (comma- or space-delimited) as a single column.

# Comma-delimited
echo "1,2,3,4,5" | statcpp desc mean --noheader --col 1 --row
#   Mean:         3

# Space-delimited
echo "1 2 3 4 5" | statcpp desc mean --noheader --col 1 --row
#   Mean:         3

All examples in this document are based on files in test/e2e/data/. You can run all examples with docs/run_reference.sh and verify the output (results: docs/output.txt).

Test Data

File	Contents
`basic.csv`	name, value (10,20,30,40,50), score
`two_groups.csv`	group1 (23,25,27,22,24,26,28,21), group2 (28,30,32,29,31,33,35,27)
`scores.csv`	math, science, english (10 subjects, 3 scores)
`forecast.csv`	actual, predicted (8 forecast accuracy records)
`survival.csv`	time (1-8), event (0/1)
`survival_two.csv`	time1, event1, time2, event2 (two-group survival data, 6 records each)
`pvalues.csv`	pvalue (0.001, 0.013, 0.04, 0.06, 0.50)
`contingency.csv`	value (30,10,20,40) — 2x2 contingency table (a,b,c,d)
`binary.csv`	x1, x2, y (binary response 0/1, 12 records)
`count.csv`	x1, x2, y (count data response, 8 records)
`missing.csv`	id, value (contains NA/empty cells)
`quoted.csv`	name, city, value (RFC 4180 quoted fields)
`noheader.csv`	No header, 3 columns x 3 rows (10,20,30 / 40,50,60 / 70,80,90)

desc - Descriptive Statistics

summary

statcpp desc summary test/e2e/data/basic.csv --col value

  Count:        5
  Mean:         30
  Std Dev:      15.8114
  Min:          10
  Q1:           20
  Median:       30
  Q3:           40
  Max:          50
  Skewness:     0
  Kurtosis:     -1.2

mean

statcpp desc mean test/e2e/data/basic.csv --col value

  Mean:         30

median

Data is automatically sorted internally (use --presorted to skip sorting).

statcpp desc median test/e2e/data/basic.csv --col value

  Median:       30

mode

All modes are displayed if there are multiple.

statcpp desc mode test/e2e/data/basic.csv --col value

  Mode[1]:      10
  Mode[2]:      20
  Mode[3]:      30
  Mode[4]:      40
  Mode[5]:      50

var

Default is sample variance (ddof=1). Use --population for population variance.

statcpp desc var test/e2e/data/basic.csv --col value

  Variance:     250

sd

statcpp desc sd test/e2e/data/basic.csv --col value

  Std Dev:      15.8114

range

statcpp desc range test/e2e/data/basic.csv --col value

  Range:        40

iqr

statcpp desc iqr test/e2e/data/basic.csv --col value

  IQR:          20

cv

Coefficient of variation (CV = SD / Mean).

statcpp desc cv test/e2e/data/basic.csv --col value

  CV:           0.527046

skewness

statcpp desc skewness test/e2e/data/basic.csv --col value

  Skewness:     0

kurtosis

Excess kurtosis. Equals 0 for a normal distribution.

statcpp desc kurtosis test/e2e/data/basic.csv --col value

  Kurtosis:     -1.2

percentile

Specify the position with --p (0.0-1.0).

statcpp desc percentile test/e2e/data/basic.csv --col value --p 0.95

  P95:          48

quartiles

statcpp desc quartiles test/e2e/data/basic.csv --col value

  Q1:           20
  Q2:           30
  Q3:           40

five-number

statcpp desc five-number test/e2e/data/basic.csv --col value

  Min:          10
  Q1:           20
  Median:       30
  Q3:           40
  Max:          50

gmean

Geometric mean.

statcpp desc gmean test/e2e/data/basic.csv --col value

  Geometric Mean:26.0517

hmean

Harmonic mean.

statcpp desc hmean test/e2e/data/basic.csv --col value

  Harmonic Mean:21.8978

trimmed-mean

Specify the trim ratio with --trim (default 0.1 = top and bottom 10%).

statcpp desc trimmed-mean test/e2e/data/basic.csv --col value

  Trimmed Mean: 30

test - Statistical Tests

--alternative (two-sided / less / greater) and --alpha (default 0.05) can be specified.

t

Automatically determined by number of columns.

# One-sample t-test
statcpp test t test/e2e/data/two_groups.csv --col group1 --mu0 25

  Statistic:    -0.57735
  df:           7
  p-value:      0.581788
  alpha:        0.05
  Fail to reject H0

# Two-sample t-test (independent)
statcpp test t test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    -4.78191
  df:           14
  p-value:      0.000292342
  alpha:        0.05
  Reject H0

Paired: add --paired.

welch

statcpp test welch test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    -4.78191
  df:           13.8979
  p-value:      0.00029825
  alpha:        0.05
  Reject H0

z

Requires population standard deviation --sigma.

statcpp test z test/e2e/data/two_groups.csv --col group1 --mu0 25 --sigma 3

f

F-test (test for equality of variances).

statcpp test f test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    0.842105
  df:           7
  p-value:      0.82645
  alpha:        0.05
  Fail to reject H0

shapiro

Shapiro-Wilk test for normality.

statcpp test shapiro test/e2e/data/two_groups.csv --col group1

  Statistic:    0.974858
  df:           8
  p-value:      0.933165
  alpha:        0.05
  Fail to reject H0

ks

Lilliefors test for normality. This is a Lilliefors test (not a standard KS test) because the mean and variance are estimated from the data.

statcpp test ks test/e2e/data/two_groups.csv --col group1

mann-whitney

statcpp test mann-whitney test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    2
  df:           16
  p-value:      0.00160361
  alpha:        0.05
  Reject H0

wilcoxon

One column for one-sample test, two columns for paired test.

statcpp test wilcoxon test/e2e/data/two_groups.csv --col group1 --mu0 25

  Statistic:    10.5
  df:           7
  p-value:      0.61017
  alpha:        0.05
  Fail to reject H0

kruskal

statcpp test kruskal test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    9.95575
  df:           1
  p-value:      0.00160348
  alpha:        0.05
  Reject H0

levene

statcpp test levene test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    0.0366492
  df:           1
  p-value:      0.85093
  alpha:        0.05
  Fail to reject H0

bartlett

statcpp test bartlett test/e2e/data/two_groups.csv --col group1,group2

  Statistic:    0.0481772
  df:           1
  p-value:      0.826266
  alpha:        0.05
  Fail to reject H0

chisq

One column for goodness-of-fit test (uniform distribution), two columns for observed/expected frequency comparison.

statcpp test chisq test/e2e/data/basic.csv --col value

corr - Correlation & Covariance

pearson

statcpp corr pearson test/e2e/data/two_groups.csv --col group1,group2

  r:            0.928587

spearman

statcpp corr spearman test/e2e/data/two_groups.csv --col group1,group2

  r:            0.928571

kendall

statcpp corr kendall test/e2e/data/two_groups.csv --col group1,group2

  r:            0.785714

cov

statcpp corr cov test/e2e/data/two_groups.csv --col group1,group2

  Covariance:   6.07143

matrix

Displays the correlation matrix for 3 or more columns.

statcpp corr matrix test/e2e/data/scores.csv --col math,science,english

                      math     science     english
  math              1.0000      0.9391      0.9260
  science           0.9391      1.0000      0.8496
  english           0.9260      0.8496      1.0000

effect - Effect Size

cohens-d

statcpp effect cohens-d test/e2e/data/two_groups.csv --col group1,group2

  Cohen's d:    -2.39096
  Interpretation: large

For one-sample, specify --mu0.

hedges-g

Small-sample corrected.

statcpp effect hedges-g test/e2e/data/two_groups.csv --col group1,group2

  Hedges' g:    -2.26054
  Interpretation: large

glass-delta

Standardized by the control group's SD.

statcpp effect glass-delta test/e2e/data/two_groups.csv --col group1,group2

cohens-h

Effect size for proportions. Specify proportions with --p1 and --p2.

statcpp effect cohens-h --p1 0.6 --p2 0.4

odds-ratio

Specify a 2x2 contingency table with 4 values (a, b, c, d) in one column.

statcpp effect odds-ratio test/e2e/data/contingency.csv --col value

  Odds Ratio:   6

risk-ratio

Specify a 2x2 contingency table with 4 values (a, b, c, d) in one column.

statcpp effect risk-ratio test/e2e/data/contingency.csv --col value

  Risk Ratio:   2.25

ci - Confidence Intervals

Specify the confidence level with --level (default 0.95).

mean

statcpp ci mean test/e2e/data/two_groups.csv --col group1

  Estimate:     24.5
  Lower:        22.4522
  Upper:        26.5478
  Level:        0.95

diff

statcpp ci diff test/e2e/data/two_groups.csv --col group1,group2

  Estimate:     -6.125
  Lower:        -8.87219
  Upper:        -3.37781
  Level:        0.95

prop

statcpp ci prop --successes 45 --trials 100

var

statcpp ci var test/e2e/data/two_groups.csv --col group1

sample-size

No CSV required.

statcpp ci sample-size --moe 0.03

  Sample Size:  1068

reg - Regression Analysis

simple

--col x,y (first column is the predictor, last is the response).

statcpp reg simple test/e2e/data/two_groups.csv --col group1,group2

  Intercept:    5.83333
  Slope:        1.0119
  R-squared:    0.862275
  Adj R-squared:0.839321
  Residual SE:  1.06997
  F-statistic:  37.565
  F p-value:    0.000862397

  Coefficients:
                Estimate    Std.Error   t-value     p-value
  Intercept     5.83333     4.06261     1.43586     0.201058
  Slope         1.0119      0.1651      6.12903     0.000862397

multiple

--col x1,x2,...,y (last column is the response).

statcpp reg multiple test/e2e/data/scores.csv --col math,science,english

  R-squared:    0.860845
  Adj R-squared:0.821086
  Residual SE:  2.99757
  F-statistic:  21.6518
  F p-value:    0.00100519

  Coefficients:
                  Estimate    Std.Error   t-value     p-value
  (Intercept)     31.3951     8.30518     3.78019     0.00689059
  math            0.764905    0.28918     2.64508     0.0331777
  science         -0.118209   0.285503    -0.414039   0.691238

predict

Compute predicted values from the regression model.

statcpp reg predict test/e2e/data/two_groups.csv --col group1,group2

residuals

Residual diagnostics.

statcpp reg residuals test/e2e/data/two_groups.csv --col group1,group2

vif

Variance Inflation Factor (multicollinearity diagnostics).

statcpp reg vif test/e2e/data/two_groups.csv --col group1,group2

  group1:       7.26083
  group2:       7.26083

anova - Analysis of Variance

oneway

statcpp anova oneway test/e2e/data/two_groups.csv --col group1,group2

  ANOVA Table:
  Source        SS          df      MS          F           p-value
  Between       150.062     1       150.062     22.8667     0.000292342
  Within        91.875      14      6.5625      0           0
  Total         241.938     15

  F-statistic:  22.8667
  p-value:      0.000292342
  eta-squared:  0.620253
  omega-squared:0.577465
  Reject H0 (alpha=0.05)

posthoc-tukey

statcpp anova posthoc-tukey test/e2e/data/scores.csv --col math,science,english

  Tukey HSD Post-hoc Comparisons:
  Comparison      Diff        p-value     Lower       Upper       Sig?
  math vs science -1.4        0.938605    -11.626     8.82601     No
  math vs english -2.4        0.830879    -12.626     7.82601     No
  science vs english-1          0.96815     -11.226     9.22601     No

posthoc-bonferroni

statcpp anova posthoc-bonferroni test/e2e/data/scores.csv --col math,science,english

posthoc-scheffe

statcpp anova posthoc-scheffe test/e2e/data/scores.csv --col math,science,english

eta-squared

statcpp anova eta-squared test/e2e/data/scores.csv --col math,science,english

  eta-squared:  0.0124993
  omega-squared:-0.058509
  Cohen's f:    0.112506

resample - Resampling

Output depends on random numbers, so results vary between runs.

bootstrap-mean

statcpp resample bootstrap-mean test/e2e/data/two_groups.csv --col group1

bootstrap-median

statcpp resample bootstrap-median test/e2e/data/two_groups.csv --col group1

bootstrap-sd

statcpp resample bootstrap-sd test/e2e/data/two_groups.csv --col group1

bca

BCa method (bias-corrected and accelerated bootstrap).

statcpp resample bca test/e2e/data/two_groups.csv --col group1

permtest

Two columns for independent two-group test; add --paired for paired test.

statcpp resample permtest test/e2e/data/two_groups.csv --col group1,group2

permtest-corr

statcpp resample permtest-corr test/e2e/data/two_groups.csv --col group1,group2

ts - Time Series Analysis

acf

Maximum lag of 20 (or data length - 1).

statcpp ts acf test/e2e/data/two_groups.csv --col group1

pacf

statcpp ts pacf test/e2e/data/two_groups.csv --col group1

ma

Moving average (window size = 3).

statcpp ts ma test/e2e/data/two_groups.csv --col group1

ema

Exponential moving average (alpha = 0.3).

statcpp ts ema test/e2e/data/two_groups.csv --col group1

diff

statcpp ts diff test/e2e/data/two_groups.csv --col group1

  d[1]:         2
  d[2]:         2
  d[3]:         -5
  d[4]:         2
  d[5]:         2
  d[6]:         2
  d[7]:         -7

mae

Two columns (actual, predicted).

statcpp ts mae test/e2e/data/forecast.csv --col actual,predicted

  MAE:          3

rmse

statcpp ts rmse test/e2e/data/forecast.csv --col actual,predicted

  RMSE:         3.24037

mape

statcpp ts mape test/e2e/data/forecast.csv --col actual,predicted

  MAPE (%):     2.47699

robust - Robust Statistics

mad

statcpp robust mad test/e2e/data/two_groups.csv --col group1

  MAD:          2
  MAD scaled:   2.9652

outliers

IQR method.

statcpp robust outliers test/e2e/data/basic.csv --col value

  Q1:           20
  Q3:           40
  IQR:          20
  Lower fence:  -10
  Upper fence:  70
  N outliers:   0

outliers-zscore

Z-score method.

statcpp robust outliers-zscore test/e2e/data/basic.csv --col value

outliers-modified

Modified Z-score method (MAD-based).

statcpp robust outliers-modified test/e2e/data/basic.csv --col value

winsorize

Specify the trim ratio with --trim.

statcpp robust winsorize test/e2e/data/basic.csv --col value --trim 0.05

hodges-lehmann

statcpp robust hodges-lehmann test/e2e/data/two_groups.csv --col group1

  Hodges-Lehmann:24.5

biweight

statcpp robust biweight test/e2e/data/two_groups.csv --col group1

  Biweight midvariance:5.88694

survival - Survival Analysis

kaplan-meier

--col time,event (event: 1=event, 0=censored).

statcpp survival kaplan-meier test/e2e/data/survival.csv --col time,event

  Kaplan-Meier Survival Estimates:
  Time      Survival    SE        At Risk   Events    Censored
  0         1           0         8         0         0
  1         0.875       0.116927  8         1         0
  3         0.729167    0.164976  6         1         0
  4         0.583333    0.185561  5         1         0
  6         0.388889    0.201269  3         1         0
  7         0.194444    0.170387  2         1         0

  Median survival: 6

logrank

Specify two-group survival data with 4 columns (time1, event1, time2, event2).

statcpp survival logrank test/e2e/data/survival_two.csv --col time1,event1,time2,event2

  Chi-square:   0.316564
  p-value:      0.57368
  df:           1
  Observed 1:   4
  Expected 1:   3.27778
  Observed 2:   4
  Expected 2:   4.72222

nelson-aalen

statcpp survival nelson-aalen test/e2e/data/survival.csv --col time,event

  Nelson-Aalen Cumulative Hazard:
  Time      Hazard      Cum.Hazard
  0         0           0
  1         0.125       0.125
  3         0.166667    0.291667
  4         0.2         0.491667
  6         0.333333    0.825
  7         0.5         1.325

cluster - Clustering

Output may depend on random numbers (kmeans). Requires 2 or more columns.

kmeans

Default k=3.

statcpp cluster kmeans test/e2e/data/scores.csv --col math,science,english

hierarchical

statcpp cluster hierarchical test/e2e/data/scores.csv --col math,science,english

silhouette

statcpp cluster silhouette test/e2e/data/scores.csv --col math,science,english

multiple - Multiple Testing Correction

bonferroni

statcpp multiple bonferroni test/e2e/data/pvalues.csv --col pvalue

  Bonferroni Correction:
  Adjusted alpha:0.01

  Test    p-value     Adj. p-value  Sig?
  1       0.001       0.005         Yes
  2       0.013       0.065         No
  3       0.04        0.2           No
  4       0.06        0.3           No
  5       0.5         1             No

holm

statcpp multiple holm test/e2e/data/pvalues.csv --col pvalue

  Holm-Bonferroni Correction:
  Test    p-value     Adj. p-value  Sig?
  1       0.001       0.005         Yes
  2       0.013       0.052         No
  3       0.04        0.12          No
  4       0.06        0.12          No
  5       0.5         0.5           No

bh

Benjamini-Hochberg (FDR control).

statcpp multiple bh test/e2e/data/pvalues.csv --col pvalue

  Benjamini-Hochberg (FDR) Correction:
  Test    p-value     Adj. p-value  Sig?
  1       0.001       0.005         Yes
  2       0.013       0.0325        Yes
  3       0.04        0.0666667     No
  4       0.06        0.075         No
  5       0.5         0.5           No

power - Power Analysis

No CSV input required. When --n is specified, power is calculated; when omitted, the required sample size is calculated.

t-one

--effect (Cohen's d) is required.

statcpp power t-one --effect 0.5 --n 30

  Power:        0.781908

statcpp power t-one --effect 0.5 --power 0.8

  Sample size:  32

t-two

Sample size ratio can be specified with --ratio.

statcpp power t-two --effect 0.5 --n 30

  Power:        0.490686

statcpp power t-two --effect 0.5 --power 0.8

  n1:           63
  n2:           63

prop

--p1 and --p2 are required.

statcpp power prop --p1 0.3 --p2 0.5 --n 50

  Power:        0.549836

statcpp power prop --p1 0.3 --p2 0.5 --power 0.8

  Sample size:  93

glm - Generalized Linear Models

logistic

--col x1,...,y (y is binary 0/1).

statcpp glm logistic test/e2e/data/binary.csv --col x1,x2,y

  Null deviance:16.3006
  Residual deviance:13.3226
  AIC:          19.3226
  Log-likelihood:-6.6613
  Iterations:   6

  Coefficients:
                  Estimate    Std.Error   z-value     p-value
  (Intercept)     0.173347    2.22534     0.0778966   0.93791
  x1              0.714642    0.580323    1.23145     0.218153
  x2              -0.796283   0.942142    -0.845183   0.398008

  Pseudo R-squared (McFadden): 0.182694
  Odds ratios: x1=0.451002 x2=2.22534

poisson

--col x1,...,y (y is count data).

statcpp glm poisson test/e2e/data/count.csv --col x1,x2,y

  Null deviance:9.16573
  Residual deviance:0.53496
  AIC:          34.4471
  Log-likelihood:-14.2236
  Iterations:   5

  Coefficients:
                  Estimate    Std.Error   z-value     p-value
  (Intercept)     0.422972    0.521971    0.810336    0.417747
  x1              0.0443169   0.0731394   0.605924    0.544565
  x2              0.138836    0.0561691   2.47175     0.0134455

  Incidence rate ratios: x1=1.14894 x2=8.79106e+252

model - Model Selection

aic

statcpp model aic test/e2e/data/scores.csv --col math,science,english

cv

Cross-validation (5-fold).

statcpp model cv test/e2e/data/scores.csv --col math,science,english

ridge

statcpp model ridge test/e2e/data/scores.csv --col math,science,english

lasso

statcpp model lasso test/e2e/data/scores.csv --col math,science,english

Output Modes

JSON Output (`--json`)

desc

statcpp desc summary test/e2e/data/basic.csv --col value --json

{
    "command": "desc.summary",
    "input": {
        "column": "value",
        "n": 5
    },
    "result": {
        "Count": 5.0,
        "Kurtosis": -1.2000000000000002,
        "Max": 50.0,
        "Mean": 30.0,
        "Median": 30.0,
        "Min": 10.0,
        "Q1": 20.0,
        "Q3": 40.0,
        "Skewness": 0.0,
        "Std Dev": 15.811388300841896
    }
}

test

statcpp test t test/e2e/data/two_groups.csv --col group1,group2 --json

{
    "command": "test.t",
    "input": {
        "columns": [
            "group1",
            "group2"
        ],
        "n1": 8,
        "n2": 8
    },
    "result": {
        "Statistic": -4.781910357447813,
        "alpha": 0.05,
        "df": 14.0,
        "p-value": 0.00029234175805803453
    }
}

corr

statcpp corr pearson test/e2e/data/two_groups.csv --col group1,group2 --json

{
    "command": "corr.pearson",
    "input": {
        "columns": [
            "group1",
            "group2"
        ],
        "n": 8
    },
    "result": {
        "r": 0.9285874942379881
    }
}

effect

statcpp effect cohens-d test/e2e/data/two_groups.csv --col group1,group2 --json

{
    "command": "effect.cohens-d",
    "input": {
        "columns": [
            "group1",
            "group2"
        ],
        "n1": 8,
        "n2": 8
    },
    "result": {
        "Cohen's d": -2.3909551787239063
    }
}

statcpp effect cohens-h --p1 0.6 --p2 0.4 --json

{
    "command": "effect.cohens-h",
    "input": {
        "p1": 0.6,
        "p2": 0.4
    },
    "result": {
        "Cohen's h": 0.40271584158066154
    }
}

ci

statcpp ci mean test/e2e/data/two_groups.csv --col group1 --json

{
    "command": "ci.mean",
    "input": {
        "column": "group1",
        "n": 8
    },
    "result": {
        "Estimate": 24.5,
        "Level": 0.95,
        "Lower": 22.452175328624975,
        "Upper": 26.547824671375025
    }
}

statcpp ci prop --successes 45 --trials 100 --json

{
    "command": "ci.prop",
    "input": {
        "successes": 45,
        "trials": 100
    },
    "result": {
        "Estimate": 0.45,
        "Level": 0.95,
        "Lower": 0.3561453797236467,
        "Upper": 0.5475539701027973
    }
}

statcpp ci sample-size --moe 0.03 --json

{
    "command": "ci.sample-size",
    "input": {
        "level": 0.95,
        "moe": 0.03
    },
    "result": {
        "Sample Size": 1068.0
    }
}

reg

statcpp reg simple test/e2e/data/two_groups.csv --col group1,group2 --json

{
    "command": "reg.simple",
    "input": {
        "n": 8,
        "x": "group1",
        "y": "group2"
    },
    "result": {
        "Adj R-squared": 0.8393205235310498,
        "F p-value": 0.0008623966761827084,
        "F-statistic": 37.56499133448873,
        "Intercept": 5.833333333333334,
        "R-squared": 0.8622747344551855,
        "Residual SE": 1.0699725556486344,
        "Slope": 1.0119047619047619
    }
}

anova

statcpp anova oneway test/e2e/data/two_groups.csv --col group1,group2 --json

{
    "command": "anova.oneway",
    "input": {
        "columns": [
            "group1",
            "group2"
        ],
        "k": 2,
        "n": 16
    },
    "result": {
        "F-statistic": 22.866666666666667,
        "eta-squared": 0.620253164556962,
        "omega-squared": 0.5774647887323944,
        "p-value": 0.00029234175805814555
    }
}

ts

statcpp ts diff test/e2e/data/two_groups.csv --col group1 --json

{
    "command": "ts.diff",
    "input": {
        "column": "group1",
        "n": 8,
        "order": 1
    },
    "result": {
        "d[1]": 2.0,
        "d[2]": 2.0,
        "d[3]": -5.0,
        "d[4]": 2.0,
        "d[5]": 2.0,
        "d[6]": 2.0,
        "d[7]": -7.0
    }
}

robust

statcpp robust mad test/e2e/data/two_groups.csv --col group1 --json

{
    "command": "robust.mad",
    "input": {
        "column": "group1",
        "n": 8
    },
    "result": {
        "MAD": 2.0,
        "MAD scaled": 2.9652
    }
}

survival

statcpp survival kaplan-meier test/e2e/data/survival.csv --col time,event --json

{
    "command": "survival.kaplan-meier",
    "input": {
        "event": "event",
        "n": 8,
        "time": "time"
    },
    "result": {
        "Median survival": 6.0,
        "N events": 5.0
    }
}

multiple

statcpp multiple bonferroni test/e2e/data/pvalues.csv --col pvalue --json

{
    "command": "multiple.bonferroni",
    "input": {
        "alpha": 0.05,
        "column": "pvalue",
        "n_tests": 5
    },
    "result": {
        "Adjusted alpha": 0.01,
        "p1 adjusted": 0.005,
        "p2 adjusted": 0.065,
        "p3 adjusted": 0.2,
        "p4 adjusted": 0.3,
        "p5 adjusted": 1.0
    }
}

power

statcpp power t-one --effect 0.5 --n 30 --json

{
    "command": "power.t-one",
    "input": {
        "alpha": 0.05,
        "alternative": "two-sided",
        "effect": 0.5,
        "n": 30
    },
    "result": {
        "Power": 0.781908063920346
    }
}

Quiet Output (`--quiet`)

statcpp desc mean test/e2e/data/basic.csv --col value --quiet

stdin Pipe

printf '1\n2\n3\n4\n5\n' | statcpp desc mean --noheader --col 1

  Mean:         3

Shortcuts

Shortcut	Expands to
`mean`	`desc mean`
`median`	`desc median`
`mode`	`desc mode`
`sd`	`desc sd`
`var`	`desc var`
`summary`	`desc summary`
`range`	`desc range`
`iqr`	`desc iqr`
`cv`	`desc cv`
`skewness`	`desc skewness`
`kurtosis`	`desc kurtosis`
`quartiles`	`desc quartiles`
`gmean`	`desc gmean`
`hmean`	`desc hmean`
`ttest`	`test t`
`pearson`	`corr pearson`
`spearman`	`corr spearman`
`kendall`	`corr kendall`

For detailed options, see the Command Reference.

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statcpp CLI Test Reference

Test Data

desc - Descriptive Statistics

summary

mean

median

mode

var

sd

range

iqr

cv

skewness

kurtosis

percentile

quartiles

five-number

gmean

hmean

trimmed-mean

test - Statistical Tests

t

welch

z

f

shapiro

ks

mann-whitney

wilcoxon

kruskal

levene

bartlett

chisq

corr - Correlation & Covariance

pearson

spearman

kendall

cov

matrix

effect - Effect Size

cohens-d

hedges-g

glass-delta

cohens-h

odds-ratio

risk-ratio

ci - Confidence Intervals

mean

diff

prop

var

sample-size

reg - Regression Analysis

simple

multiple

predict

residuals

vif

anova - Analysis of Variance

oneway

posthoc-tukey

posthoc-bonferroni

posthoc-scheffe

eta-squared

resample - Resampling

bootstrap-mean

bootstrap-median

bootstrap-sd

bca

permtest

permtest-corr

ts - Time Series Analysis

acf

pacf

JSON Output (`--json`)

Quiet Output (`--quiet`)