equitrends

Equivalence Tests for Pre-Trends in DiD Estimation

EQUITRENDS is a Stata package implementing equivalence tests for pre-trends in Difference-in-Differences (DiD) designs, based on Dette & Schumann (2024), published in the Journal of Business & Economic Statistics.

Overview

Standard pre-trend tests in DiD designs test the null hypothesis of exact parallel trends (H₀: β = 0). This approach suffers from fundamental limitations:

Failure to reject ≠ Evidence in favor: Low statistical power may prevent detection of actual violations. Failing to reject does not support the parallel trends assumption.
Conditional bias amplification: Roth (2022) demonstrates that conditioning on passing traditional pre-tests can amplify DiD bias when violations exist.
No explicit threshold: Traditional tests provide no framework for determining what constitutes a "negligible" deviation from parallel trends.

EQUITRENDS implements equivalence tests that reverse the burden of proof: the null hypothesis is that deviations are large (H₀: ‖β‖ ≥ threshold), and rejection provides statistical evidence that deviations are small. This approach:

Requires explicit justification of the equivalence threshold
Controls Type I error (falsely concluding equivalence)
Increases statistical power with sample size
Allows researchers to quantify the smallest threshold at which equivalence holds

Key features

Three equivalence hypotheses: maximum, mean, and RMS (Dette & Schumann, 2024, Section 3.1)
Minimum equivalence threshold: compute the smallest threshold at which equivalence can be concluded
Multiple inference methods for the maximum test: IU (analytical), spherical bootstrap, and wild bootstrap
Visualization: coefficient plots with equivalence bounds (equivtest_plot)

Requirements

Stata 16.0 or higher
Python (Stata Python integration) with numpy and scipy for bootstrap-based maximum tests:
- equivtest ..., type(max) method(boot)
- equivtest ..., type(max) method(wild)

Note: The IU method for type(max) and the type(mean) / type(rms) tests do not require Python. Bootstrap methods (method(boot) and method(wild)) require Python with numpy and scipy.

Installation

From GitHub

* Install the package
net install equitrends, from("https://raw.githubusercontent.com/gorgeousfish/equitrends/main") replace

Local installation

* Install the package
net install equitrends, from("/path/to/equitrends-main") replace

Loading example data

* Load the bundled example dataset
equitrends_data, clear

The dataset is bundled with the package and loaded from the local installation directory.

Getting help

After installation, access the built-in Stata documentation:

help equitrends       // Package overview and command list
help equivtest        // Main unified testing interface
help equivtest_plot   // Visualization options
help maxequivtest     // Maximum test details
help meanequivtest    // Mean test details
help rmsequivtest     // RMS test details
help equivsim         // Monte Carlo simulation
help equitrends_data  // Load example datasets

Data requirements

Before using this package, ensure your data meets the following requirements:

Requirement	Description
Panel structure	Panel data with individual (`id`) and time (`time`) identifiers. Both balanced and unbalanced panels are supported.
Minimum pre-treatment periods	At least one pre-treatment period (T ≥ 1). More periods increase statistical power.
Treatment group indicator	Binary variable coded as 0 (control) or 1 (treated).
Block adoption design	All treated units must receive treatment at the same time. Staggered adoption requires cohort-specific analysis (see Dette & Schumann, 2024, Section 5).
Complete time-group cells	Each time period must contain observations in both treatment and control groups.

Unbalanced panels

The package automatically detects and handles unbalanced panels (where individuals have different numbers of observed time periods). When an unbalanced panel is detected:

e(is_balanced) returns 0
e(T_min) and e(T_max) store the minimum and maximum number of periods per individual
Output displays "Panel type: Unbalanced" with the period range

No special syntax is required—simply run the command as usual:

xtset id time  // Shows "unbalanced" if panel is unbalanced
equivtest y, type(max) id(id) group(treat) time(time) pretreatment(1 2 3 4 5) baseperiod(5)

Data completeness

When some time periods lack observations for either group, the placebo regression cannot be estimated correctly. In such cases:

The command will issue an error or produce missing values
Check that each time-group cell contains at least one observation

Common causes:

Missing values in treatment, outcome, or identifier variables
Sample restrictions that eliminate entire time periods for one group

Solution: Ensure at least one observation per time-treatment cell, or restrict analysis to time periods with complete coverage. Use xtset to verify panel structure before running equivalence tests.

Quick start

Empirical example: Di Tella & Schargrodsky (2004)

This example replicates the empirical application in Dette & Schumann (2024, Section 7) using the Buenos Aires crime data from Di Tella & Schargrodsky (2004). The dataset contains monthly car theft counts for 876 Buenos Aires city blocks (April–December 1994), of which 37 blocks received police protection after a July terrorist attack.

* Load and prepare data
equitrends_data, clear
drop if mes == 72 | mes == 73                        // Remove post-treatment months
drop if mes > 7                                      // Keep pre-treatment only
rename (observ totrob mes) (ID Y period)
gen G = (distanci == 0)                              // Treatment indicator
xtset ID period

* Maximum test (IU method, cluster-robust SE)
equivtest Y, type(max) method(iu) id(ID) group(G) time(period) ///
    pretreatment(4 5 6 7) baseperiod(7) vce(cluster) cluster(ID)

* Mean test
equivtest Y, type(mean) id(ID) group(G) time(period) ///
    pretreatment(4 5 6 7) baseperiod(7) vce(cluster) cluster(ID)

* RMS test
equivtest Y, type(rms) id(ID) group(G) time(period) ///
    pretreatment(4 5 6 7) baseperiod(7) seed(2024)

* Visualize results
equivtest_plot, ci

Bootstrap methods (maximum test only)

* Spherical bootstrap (assumes spherical errors; Theorem 1)
equivtest Y, type(max) method(boot) id(ID) group(G) time(period) ///
    pretreatment(4 5 6 7) baseperiod(7) nboot(1000) seed(12345)

* Wild bootstrap (recommended for non-spherical errors; Remark 1(c))
equivtest Y, type(max) method(wild) id(ID) group(G) time(period) ///
    pretreatment(4 5 6 7) baseperiod(7) nboot(1000) seed(12345)

Control variables (conditional parallel trends)

The x() option includes additional control variables in the TWFE placebo regression (Dette & Schumann, 2024, Section 5). Time-invariant covariates are absorbed by the individual fixed effects during double demeaning. For the full conditional PTA specification (Eq. 5.5), construct covariate-by-time interactions in your data and pass them via x().

equivtest Y, type(max) method(iu) id(ID) group(G) time(period) ///
    pretreatment(4 5 6 7) baseperiod(7) x(edpub estserv banco) ///
    vce(cluster) cluster(ID)

Command reference

Command	Description
`equivtest`	Unified interface for all three tests (recommended)
`maxequivtest`	Maximum absolute coefficient test (IU/Boot/Wild)
`meanequivtest`	Mean coefficient test
`rmsequivtest`	Root mean square (RMS) test
`equivtest_plot`	Coefficient plot with equivalence bounds
`equivsim`	Monte Carlo simulation for power analysis
`equitrends_data`	Load bundled example datasets

Unified syntax

equivtest depvar, type(max|mean|rms) id(varname) group(varname) time(varname) [options]

Core options

Option	Description
`type(max/mean/rms)`	Test type (required)
`id(varname)`	Panel identifier (required)
`group(varname)`	Treatment group indicator 0/1 (required); also accepts `g()`
`time(varname)`	Time period variable (required); also accepts `period()`
`threshold(#)`	Equivalence threshold; omit to compute minimum threshold
`alpha(#)`	Significance level; default 0.05
`pretreatment(numlist)`	Pre-treatment periods to include
`baseperiod(#)`	Base period for placebo construction
`x(varlist)`	Control variables

Options specific to type(max):

Option	Description
`method(iu/boot/wild)`	Inference method; default `iu`
`nboot(#)`	Bootstrap replications; default 1000
`seed(#)`	Random seed for bootstrap
`nodots`	Suppress bootstrap progress display

Options specific to type(rms):

Option	Description
`nolambda(#)`	Number of subsamples used for self-normalization; default 5
`seed(#)`	Random seed for subsampling

Robust SE options (available for IU/mean; not for bootstrap or RMS):

Option	Description
`vce(vcetype)`	Variance estimator; see table below
`cluster(varname)`	Cluster variable (required for cluster-robust VCE types)

Variance estimator types (vcetype):

Type	Description
`ols`	Homoskedastic OLS variance (default)
`robust`/`hc1`	HC1 heteroskedasticity-robust (White, 1980)
`hc2`	HC2 leverage-adjusted (MacKinnon & White, 1985); better finite-sample properties
`hc3`	HC3 more conservative leverage adjustment (Davidson & MacKinnon, 1993)
`hac`	Arellano (1987) HAC estimator for panel data
`cluster`/`cr0`	CR0 cluster-robust without small-sample adjustment; requires `cluster()`
`cr1`	CR1 cluster-robust with G/(G-1) adjustment (Stata default); requires `cluster()`
`hc1_cluster`	HC1 cluster-robust with small-sample adjustment; requires `cluster()`

Test selection guide

EQUITRENDS offers three equivalence tests with different properties. Choose based on your research context:

Feature	Maximum test	Mean test	RMS test
Hypothesis	max\|βₜ\| < δ	\|β̄\| < τ	β_RMS < ζ
Measures	Largest single violation	Average violation	Root mean square
Cancellation	No	Yes (opposing signs cancel)	No
Sensitivity	Any single large deviation	Systematic directional bias	Balanced across deviations
Conservativeness	Most conservative	Least conservative	Moderate

Recommendations

Maximum test (type(max)): Start here as the default, conservative choice.
- Detects any single large violation
- Use method(iu) for analytical inference or method(wild) for non-spherical errors
- Recommended when you want to rule out any substantial pre-trend violation
Mean test (type(mean)): Use when violations are expected to be monotone (same sign).
- More powerful when deviations are directionally consistent
- Caution: Opposing violations may cancel out, leading to false equivalence
RMS test (type(rms)): Use as a general-purpose alternative.
- Balances sensitivity across all placebo coefficients
- No cancellation problem
- Self-normalized (no variance estimation required)

Interpreting minimum thresholds

When threshold() is omitted, EQUITRENDS reports the smallest equivalence threshold (δ*, τ*, or ζ*) at which equivalence can be concluded at the specified significance level. Compare this to your estimated treatment effect:

δ* << estimated ATT: Strong evidence for negligible pre-trends
δ* ≈ estimated ATT: Pre-trend violations may explain the treatment effect
δ* >> estimated ATT: Insufficient evidence for parallel trends; consider alternative designs

Methodology

Let $\beta = (\beta_1,\ldots,\beta_T)'$ denote the vector of placebo (pre-treatment) coefficients from the TWFE placebo regression (Dette & Schumann, 2024, Eq. (2.5)). EQUITRENDS implements three equivalence hypotheses (Section 3.1):

Maximum deviation (Eq. (3.1)):

$$ H_0: |\beta|_{\infty} \ge \delta \quad \text{vs.} \quad H_1: |\beta|_{\infty} < \delta, \qquad |\beta|_{\infty}=\max_{l\in{1,\ldots,T}}|\beta_l| $$

Mean deviation (Eq. (3.2)):

$$ \bar{\beta}=\frac{1}{T}\sum_{l=1}^{T}\beta_l, \qquad H_0: |\bar{\beta}| \ge \tau \quad \text{vs.} \quad H_1: |\bar{\beta}| < \tau $$

RMS deviation (Eq. (3.3)):

$$ \beta_{\mathrm{RMS}}=\sqrt{\frac{1}{T}\sum_{l=1}^{T}\beta_l^2}, \qquad H_0: \beta_{\mathrm{RMS}} \ge \zeta \quad \text{vs.} \quad H_1: \beta_{\mathrm{RMS}} < \zeta $$

Inference methods for the maximum test

IU (Intersection-Union, analytical): For each placebo coefficient t = 1, ..., T, the test rejects H0 iff all |beta_t| < Q(alpha), where Q denotes the alpha-quantile of the folded normal distribution with mean delta and variance sigma_tt/n (Dette & Schumann, 2024, Eq. (4.4)). Computationally attractive but conservative for large T. Folded normal CDF/quantiles are implemented in Mata.
Bootstrap (method(boot)): Generates bootstrap samples under the constraint on beta using constrained OLS, then computes the empirical alpha-quantile as the critical value (Dette & Schumann, 2024, Theorem 1). Assumes spherical errors. More powerful than IU for T > 1. Requires Python with numpy and scipy.
Wild bootstrap (method(wild)): Replaces i.i.d. bootstrap errors with Rademacher-weighted residuals, making the test robust to heteroskedasticity and serial correlation (Dette & Schumann, 2024, Remark 1(c)). Recommended for non-spherical errors. Requires Python with numpy and scipy.

Inference for the mean test

The mean test rejects H0 whenever the absolute sample mean of placebo coefficients falls below the alpha-quantile of the folded normal with mean tau and variance 1'Sigma1/(nT^2) (Dette & Schumann, 2024, Eq. (4.12)).

Inference for the RMS test

The RMS test uses a self-normalized statistic based on subsampling (Dette & Schumann, 2024, Theorem 2). It rejects H0 whenever beta_RMS^2 < zeta^2 + Q_W(alpha) * V_n, where Q_W(alpha) is the alpha-quantile of the limiting distribution (a functional of Brownian motion) and V_n is computed from subsample estimates (Eq. (4.18)). This test is pivotal and does not require variance estimation.

RMS test alpha restriction

The RMS test supports only:

$$ \alpha \in {0.01, 0.025, 0.05, 0.1, 0.2} $$

This reflects the implementation based on critical values for the limiting distribution in Dette & Schumann (2024, Theorems 2-3).

Stored results

equivtest stores results in e() (see also src/sthlp/equivtest.sthlp).

Scalars

Result	Description
`e(N)`	Number of observations
`e(N_g)`	Number of individuals (panels)
`e(no_placebos)`	Number of placebo coefficients (T)
`e(alpha)`	Significance level used
`e(base_period)`	Base period for placebo construction
`e(is_balanced)`	1 if balanced panel, 0 otherwise
`e(threshold_specified)`	1 if `threshold()` was specified, 0 otherwise
`e(threshold)`	Equivalence threshold value (if specified)
`e(reject)`	1 if H₀ rejected, 0 otherwise (if threshold specified)
`e(min_threshold)`	Minimum threshold δ/τ/ζ* at which equivalence holds (if threshold omitted)

Type-specific scalars for type(max):

Result	Description
`e(max_abs_coef)`	Maximum absolute placebo coefficient
`e(nboot)`	Number of bootstrap replications (`method(boot)` or `wild`)
`e(boot_critical)`	Bootstrap critical value (`method(boot)` or `wild`)

Type-specific scalars for type(mean):

Result	Description
`e(abs_mean_placebo)`	Absolute value of mean placebo coefficient
`e(var_mean_placebo)`	Variance of mean placebo coefficient
`e(se_mean_placebo)`	Standard error of mean placebo coefficient
`e(p_value)`	p-value (if threshold specified)
`e(mean_critical_value)`	Critical value for mean test (if threshold specified)

Type-specific scalars for type(rms):

Result	Description
`e(rms_placebo_coefs)`	Root mean square of placebo coefficients
`e(nolambda)`	Number of lambda subsamples
`e(rms_critical_value)`	RMS critical value (if threshold specified)

Macros

Result	Description
`e(cmd)`	Command name (`equivtest`)
`e(cmdline)`	Full command as typed
`e(type)`	Test type: `max`, `mean`, or `rms`
`e(method)`	Inference method: `iu`, `boot`, or `wild` (only for `type(max)`)
`e(depvar)`	Dependent variable name
`e(idvar)`	Panel identifier variable
`e(groupvar)`	Treatment group variable
`e(timevar)`	Time variable
`e(vce)`	Variance estimator type (when applicable)
`e(clustvar)`	Cluster variable (if cluster-robust VCE)
`e(preperiods)`	Specified pre-treatment periods (if `pretreatment()` used)
`e(placebo_coef_names)`	Names of placebo coefficient periods

Matrices

Result	Description
`e(b_placebo)`	Placebo coefficient vector (T × 1); available for IU max and mean tests
`e(V_placebo)`	Placebo variance-covariance matrix (T × T); available for IU max/mean tests
`e(se_placebo)`	Placebo standard errors (T × 1); available for IU max test
`e(IU_critical_values)`	Critical values per coefficient (type=max, method=iu, threshold specified)
`e(min_equiv_thresholds)`	Minimum thresholds per coefficient (type=max, method=iu, threshold not specified)

Visualization

equivtest_plot creates coefficient plots of placebo coefficients with equivalence bounds. Run it after equivtest to visualize results.

Basic syntax

equivtest_plot [, options]

Main options

Option	Description
`threshold(#)`	Equivalence threshold for horizontal lines; defaults to `e(threshold)` or `e(min_threshold)`
`ci`	Display confidence intervals (IU method only)
`level(#)`	Confidence level; default 95
`connect`	Connect points with lines
`noline`	Suppress zero reference line
`nobase`	Suppress base period reference point
`nothreshold`	Suppress equivalence threshold lines

Style options

Option	Description
`msymbol(symbolstyle)`	Marker symbol; default `O` (circle)
`msize(markersizestyle)`	Marker size; default `medium`
`mcolor(colorstyle)`	Marker color; default `navy`
`basemsymbol(symbolstyle)`	Base period marker symbol; default `S`
`basemcolor(colorstyle)`	Base period marker color
`threshlcolor(colorstyle)`	Threshold line color; default `red`
`threshlwidth(linewidthstyle)`	Threshold line width; default `medium`
`threshlpattern(linepatternstyle)`	Threshold line pattern; default `dash`
`cilcolor(colorstyle)`	CI line color; default `navy`
`cilwidth(linewidthstyle)`	CI line width; default `medium`

Output options

Option	Description
`title(string)`	Graph title
`subtitle(string)`	Graph subtitle
`xtitle(string)`	X-axis title
`ytitle(string)`	Y-axis title
`xlabel(rule_or_values)`	X-axis labels
`ylabel(rule_or_values)`	Y-axis labels
`note(string)`	Graph note
`scheme(schemename)`	Graph scheme
`saving(filename)`	Save graph to file
`replace`	Replace existing file when saving
`name(windowname)`	Graph window name

Examples

* Basic plot after running equivtest
equivtest_plot

* Plot with 95% confidence intervals
equivtest_plot, ci

* Publication-quality plot
equivtest_plot, ci level(95) title("Pre-trend Analysis") ///
    msymbol(O) mcolor(navy) threshlpattern(dash) scheme(s2color)

* Save graph to file
equivtest_plot, ci saving(pretrend_plot) replace

Examples

Simulated panel data

This example demonstrates the package using simulated data with parallel trends satisfied.

* Generate simulated panel data
clear
set seed 12345
set obs 1000
gen id = ceil(_n/10)
gen time = mod(_n-1, 10) + 1
gen treat = (id <= 50)

* Generate outcome: parallel trends hold in pre-treatment (time <= 5)
* Treatment effect of 0.5 in post-treatment (time > 5)
gen y = rnormal() + 0.1*time + treat*(time > 5)*0.5

* Run maximum test with IU method
equivtest y, type(max) method(iu) id(id) group(treat) time(time) ///
    pretreatment(1 2 3 4) baseperiod(5)

* Display minimum threshold
display "Minimum equivalence threshold (delta*): " %6.4f e(min_threshold)

Interpreting results

When running equivtest without specifying a threshold(), the command reports the minimum equivalence threshold at which the null hypothesis of non-equivalence can be rejected:

Equivalence Test for Pre-Trends (Maximum)
──────────────────────────────────────────────────────────────────────────────
Type:             max                    Observations:       1000
Method:           iu                     Individuals:         100
VCE:              ols                    Placebo coefs:         4
──────────────────────────────────────────────────────────────────────────────

Hypothesis Test:
  H0: max|placebo effect| >= delta  (non-equivalence)
  H1: max|placebo effect| <  delta  (equivalence)

Minimum Equivalence Threshold Search
──────────────────────────────────────────────────────────────────────────────
        Period |  Abs. Estimate   Std. Error   Min. Threshold
───────────────+──────────────────────────────────────────────────────────────
    placebo_1  |       0.034521     0.100234       0.199543
    placebo_2  |       0.012345     0.098765       0.174832
    placebo_3  |       0.056789     0.101234       0.223456
    placebo_4  |       0.023456     0.099876       0.187654
──────────────────────────────────────────────────────────────────────────────

Minimum equivalence threshold delta* =   0.2235

Interpretation: At alpha = 0.05, we can conclude that the maximum absolute placebo coefficient is less than delta*. If this threshold is small relative to your estimated treatment effect, you have evidence supporting negligible pre-trend violations.

Testing with a pre-specified threshold

* Test whether max violation < 0.2
equivtest y, type(max) method(iu) id(id) group(treat) time(time) ///
    pretreatment(1 2 3 4) baseperiod(5) threshold(0.2)

* Check rejection decision
if e(reject) == 1 {
    display "Equivalence concluded: max|beta_t| < 0.2"
}
else {
    display "Cannot conclude equivalence at threshold 0.2"
}

Citation

If you use this package in your research, please cite both the methodology paper and the Stata implementation:

APA Format:

Dette, H., & Schumann, M. (2024). Testing for Equivalence of Pre-Trends in Difference-in-Differences Estimation. Journal of Business & Economic Statistics, 42(4), 1289–1301. https://doi.org/10.1080/07350015.2024.2308121

Cai, X., & Xu, W. (2025). Equitrends: Stata module for equivalence tests for pre-trends in DiD (Version 0.1.0) [Computer software]. GitHub. https://github.com/gorgeousfish/equitrends

BibTeX:

@article{dette2024testing,
  title={Testing for Equivalence of Pre-Trends in Difference-in-Differences Estimation},
  author={Dette, Holger and Schumann, Martin},
  journal={Journal of Business \& Economic Statistics},
  volume={42},
  number={4},
  pages={1289--1301},
  year={2024},
  publisher={Taylor \& Francis},
  doi={10.1080/07350015.2024.2308121}
}

@software{equitrends2025stata,
  title={Equitrends: Stata module for equivalence tests for pre-trends in DiD},
  author={Cai, Xuanyu and Xu, Wenli},
  year={2025},
  version={0.1.0},
  url={https://github.com/gorgeousfish/equitrends}
}

Authors

Stata Implementation:

Xuanyu Cai, City University of Macau Email: xuanyuCAI@outlook.com
Wenli Xu, City University of Macau Email: wlxu@cityu.edu.mo

Methodology:

Holger Dette, Department of Mathematics, Ruhr University Bochum
Martin Schumann, School of Business and Economics, Maastricht University

License

AGPL-3.0 License. See LICENSE.

Future roadmap

The development team is evaluating the following extensions for future versions:

Staggered Adoption Support: Extending the equivalence testing framework to staggered treatment adoption designs via cohort stacking (Dette & Schumann, 2024, Section 5).
Threshold Sensitivity Analysis: Visualizing how the test decision and minimum threshold vary across a continuous range of equivalence thresholds.
Additional Bootstrap Methods: Implementing multiplier bootstrap and subsampling alternatives.
Bootstrap Parallelization: Multi-core bootstrap computation via the parallel package for reduced runtime on large datasets.

Related packages

EquiTrends (R): TiesBos/EquiTrends — Original R implementation of equivalence tests for pre-trends (Dette & Schumann, 2024)
pretest (Stata): gorgeousfish/pretest — Conditional extrapolation pre-test with bias-adjusted confidence intervals for DiD (Mikhaeil & Harshaw, 2025)

Recommended resources

For beginners in causal inference and econometrics:

Causal Inference for the Brave and True — An excellent introductory tutorial on causal inference by Matheus Facure
Causal Inference for the Brave and True (Chinese Edition) — Chinese translation by Wenzhe Huang and Wenli Xu
What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature — Comprehensive review by Roth, Sant'Anna, Bilinski & Poe (2023)

Support

For questions and bug reports, please open an issue on GitHub.

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examples		examples
images		images
src		src
.gitignore		.gitignore
README.md		README.md
README_CN.md		README_CN.md
equitrends.pkg		equitrends.pkg
stata.toc		stata.toc

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equitrends

Contents

Overview

Key features

Requirements

Installation

From GitHub

Local installation

Loading example data

Getting help

Data requirements

Unbalanced panels

Data completeness

Quick start

Empirical example: Di Tella & Schargrodsky (2004)

Bootstrap methods (maximum test only)

Control variables (conditional parallel trends)

Command reference

Unified syntax

Core options

Test selection guide

Recommendations

Interpreting minimum thresholds

Methodology

Inference methods for the maximum test

Inference for the mean test

Inference for the RMS test

RMS test alpha restriction

Stored results

Scalars

Macros

Matrices

Visualization

Basic syntax

Main options

Style options

Output options

Examples

Examples

Simulated panel data

Interpreting results

Testing with a pre-specified threshold

Citation

Authors

License

Future roadmap

Related packages

Recommended resources

Support

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