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statcpp CLI Test Reference

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

30

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.