### Install and Run csdid2 Analysis in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/README.md

This snippet demonstrates how to install the csdid2 package, load data, run the core analysis, and then use the `estat` command to display results. It covers basic usage and the default output.

```stata
* loads data from a repository
ssc install frause
frause mpdta, clear
* This will generate everything, but show nothing! unless you request it.
* this can be done using the options agg(attgt) or agg(group) etc
. csdid2 lemp, ivar(countyreal) tvar(year) gvar(first)
Always Treated units have been excluded
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
............
* However after that is done, you can just use estat to produce outcomes you want

. estat event
```

--------------------------------

### Run csdid2 Estat Command Directly in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/README.md

This example demonstrates how to directly invoke the `estat` command for event study analysis using csdid2, bypassing the initial `csdid2` command for analysis execution.

```stata
csdid2 event, estat
```

--------------------------------

### Install and Run CSDID for DDD Analysis in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/ddd_csdid.ipynb

This snippet shows how to install the 'frause' package, load the 'mpdta' dataset, generate a treatment group variable, and then run the 'csdid' command twice for two different groups. It saves the results to 'f1.dta' and 'f2.dta'.

```stata
qui:ssc install frause, replace
frause mpdta, clear
** Assume two groups, based on lpop
gen g1=lpop>3.3
** Now use csdid to estimate the model, without covariates
csdid lemp if g1==0, ivar(countyreal) time(year) gvar(first_treat) saverif(f1) replace
csdid lemp if g1==1, ivar(countyreal) time(year) gvar(first_treat) saverif(f2) replace
```

--------------------------------

### Install Marginal Histogram Dependencies

Source: https://github.com/friosavila/stpackages/blob/main/f_stat/graphs_fstat_sim_marhist_v2_270323.txt

Installs the 'palette' and 'colrspace' packages required by the Marginal Histogram Stata command. It's recommended to update existing packages as well.

```stata
ssc install palettes, replace
ssc install colrspace, replace
ado update, update
```

--------------------------------

### JavaScript: Initialize MathJax or KaTeX for Math Typesetting

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This JavaScript code initializes math typesetting for a given HTML element. It checks for the presence of `window.MathJax` or `window.katex` and calls their respective rendering functions. It's designed to be used within the Quarto publishing system.

```javascript
const typesetMath = (el) => {
  if (window.MathJax) {
    // MathJax Typeset
    window.MathJax.typeset([el]);
  } else if (window.katex) {
    // KaTeX Render
    var mathElements = el.getElementsByClassName("math");
    var macros = [];
    for (var i = 0; i < mathElements.length; i++) {
      var texText = mathElements[i].firstChild;
      if (mathElements[i].tagName == "SPAN") {
        window.katex.render(texText.data, mathElements[i], {
          displayMode: mathElements[i].classList.contains('display'),
          throwOnError: false,
          macros: macros,
          fleqn: false
        });
      }
    }
  }
}
window.Quarto = { typesetMath };
```

--------------------------------

### JavaScript: Initialize Citation Hover Popovers

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This script iterates through bibliography references on the page and sets up hover popovers using the tippy.js library. When a reference is hovered, it displays a popup containing the full citation details fetched from elements with IDs like 'ref-citekey'.

```javascript
var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
for (var i=0; i<bibliorefs.length; i++) {
  const ref = bibliorefs[i];
  const citeInfo = findCites(ref);
  if (citeInfo) {
    tippyHover(citeInfo.el, function() {
      var popup = window.document.createElement('div');
      citeInfo.cites.forEach(function(cite) {
        var citeDiv = window.document.createElement('div');
        citeDiv.classList.add('hanging-indent');
        citeDiv.classList.add('csl-entry');
        var biblioDiv = window.document.getElementById('ref-' + cite);
        if (biblioDiv) {
          citeDiv.innerHTML = biblioDiv.innerHTML;
        }
        popup.appendChild(citeDiv);
      });
      return popup.innerHTML;
    });
  }
}
```

--------------------------------

### Estimate Treatment Heterogeneity with jwdid

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

This snippet shows how to estimate heterogeneous treatment effects using the jwdid command with the 'hettype()' option. It covers time, cohort, and event (relative time) heterogeneity. If no option is specified, it defaults to full cohort-time heterogeneity.

```stata
jwdid y, ivar(i) tvar(t) gvar(g) hettype(time)
jwdid y, ivar(i) tvar(t) gvar(g) hettype(cohort)
jwdid y, ivar(i) tvar(t) gvar(g) hettype(event)
```

--------------------------------

### Plot estat Aggregation Results (Tight Axis)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Applies a 'tight' option to the x-axis of plots generated by 'estat plot' in Stata for cohort, group, or event aggregations. This recodes the x-axis values to start from 1, effectively removing blank spaces and improving the plot's presentation.

```Stata
estat plot, tight
```

--------------------------------

### Handle Cross-References with Tooltips and Dynamic Content Loading (JavaScript)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This snippet implements functionality for handling cross-references (`xrefs`). It attaches tooltips using `tippyHover` to each cross-reference element. When a tooltip is shown, it attempts to retrieve content either from an element with a matching ID on the current page or by fetching and parsing external HTML content. It processes the retrieved content using `processXRef` and displays it in the tooltip.

```javascript
for (var i=0; i<xrefs.length; i++) { const xref = xrefs[i]; tippyHover(xref, undefined, function(instance) { instance.disable(); let url = xref.getAttribute('href'); let hash = undefined; if (url.startsWith('#')) { hash = url; } else { try { hash = new URL(url).hash; } catch {} }
if (hash) { const id = hash.replace(/^#\/?/, ""); const note = window.document.getElementById(id); if (note !== null) { try { const html = processXRef(id, note.cloneNode(true)); instance.setContent(html); } finally { instance.enable(); instance.show(); } } else { // See if we can fetch this fetch(url.split('#')[0]) .then(res => res.text()) .then(html => { const parser = new DOMParser(); const htmlDoc = parser.parseFromString(html, "text/html"); const note = htmlDoc.getElementById(id); if (note !== null) { const html = processXRef(id, note); instance.setContent(html); } }).finally(() => { instance.enable(); instance.show(); }); }
} else { // See if we can fetch a full url (with no hash to target) // This is a special case and we should probably do some content thinning / targeting fetch(url) .then(res => res.text()) .then(html => { const parser = new DOMParser(); const htmlDoc = parser.parseFromString(html, "text/html"); const note = htmlDoc.querySelector('main.content'); if (note !== null) { // This should only happen for chapter cross references // (since there is no id in the URL) // remove the first header if (note.children.length > 0 && note.children[0].tagName === "HEADER") { note.children[0].remove(); } const html = processXRef(null, note); instance.setContent(html); } }).finally(() => { instance.enable(); instance.show(); }); }
}, function(instance) { });
}
```

--------------------------------

### Draw Normal Distribution Samples - Python

Source: https://github.com/friosavila/stpackages/blob/main/uci/CUI_stata.ipynb

This snippet demonstrates how to draw samples from a normal distribution using Python within the stpackages framework. It utilizes the 'drawnorm' command to generate 'x1' through 'x8' based on a covariance matrix 'V' and a sample size of 9999.

```python
capture frame create scu
frame scu: {
    drawnorm x1 x2 x3 x4 x5 x6 x7 x8, cov(V) n(9999)
}
```

--------------------------------

### Estimate Poisson Model using jwdid

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Demonstrates how to specify a Poisson regression model within the jwdid command for estimating nonlinear ETWFE models. This leverages the method() option to pass the 'poisson' model specification directly to the underlying estimation function.

```Stata
jwdid y, ivar(i) tvar(t) gvar(g) never method(poisson)
```

--------------------------------

### Plot estat Aggregation Results (Advanced Styling)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Allows for detailed customization of plots for estat aggregation results in Stata, including line styles, colors, and widths for pre and post-treatment periods. Options like 'pstyle#', 'color#', 'lwidth#', and 'barwidth#' can be used. The '#2' options are specific to 'event' aggregations.

```Stata
estat plot, pstyle1(str) color1(str) pstyle2(str) color2(str) lwidth1(str) lwidth2(str) barwidth1(str) barwidth2(str)
```

--------------------------------

### Store estat Results in Memory (post)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Posts the results of the estat command to be the current estimations in memory ('e()') in Stata, similar to the 'margins' command. This allows the results to be saved or used in subsequent analyses. This option is useful for immediate post-estimation tasks.

```Stata
estat [aggregation], post
```

--------------------------------

### Estimate Model Coefficients and Variance-Covariance Matrix (Python)

Source: https://github.com/friosavila/stpackages/blob/main/uci/CUI_stata.ipynb

This Python code snippet estimates a statistical model using the `jwdid` command, extracts the coefficients into a matrix `b`, and the variance-covariance matrix into a matrix `V`. It requires the `statsmodels` library and assumes data is loaded and preprocessed.

```python
frause mpdta, clear
jwdid lemp lpop, ivar( countyreal) tvar( year) gvar( first_treat) never
estat event, post
matrix b=e(b)
matrix V=e(V)
```

--------------------------------

### Specify Custom Weights in estat Command

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Allows users to specify custom weights for the estimation of aggregated Average Treatment Effects (ATTEs) in Stata. This option is used when the default weights derived from the model estimation are not suitable for the analysis. The syntax involves appending 'pw = weight' to the estat command.

```Stata
estat [aggregation] [pw = weight]
```

--------------------------------

### Plot estat Aggregation Results (Basic Style)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Generates a plot of the estimated aggregated effects (time, group, or event) from the estat command in Stata. The 'style()' option allows selection of plot types like 'rspike', 'rarea', 'rcap', or 'rbar', with 'rspike' being the default. Further styling options are available.

```Stata
estat plot, style(style)
```

--------------------------------

### Marginal Histogram Stata Command Syntax

Source: https://github.com/friosavila/stpackages/blob/main/f_stat/graphs_fstat_sim_marhist_v2_270323.txt

Defines the syntax for the sim_marhist command in Stata, specifying variable lists, weights, and various options for customizing scatter plots and histograms.

```stata
sim_marhist {varlist} {ifin} [{it:{help weight:weight}}] [,{cmd:{it:Twoway_Options}} {cmd:{it:lowess}} {cmd:{it:lowess_options}({it:{help twoway lowess##options:lowess_opts}})} {cmd:scatter_options}({it:options}) {cmd:hhistogram_options}([{it:{help histogram##continuous_opts:continuous_opts}} {c |} {it:{help histogram##discrete_opts:discrete_opts}}] {it:{help histogram##options:options}}) {cmd:vhistogram_options}([{it:{help histogram##continuous_opts:continuous_opts}} {c |} {it:{help histogram##discrete_opts:discrete_opts}}] {it:{help histogram##options:options}})]
```

--------------------------------

### Estimate Model with Specific Covariate Interactions (Stata)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

This command estimates a model where covariates can be interacted with time, group, or neither, offering granular control over heterogeneity. It requires specifying the dependent variable and the variables for time, group, and covariate interactions.

```stata
jwdid y , ivar(i) tvar(t) gvar(g) never exogvar(x_ex) xtvar(x_t) xgvar(x_g)
```

--------------------------------

### Highlight Code Lines Based on Annotations (JavaScript)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This JavaScript snippet defines functions for selecting and highlighting specific lines within code blocks based on annotations. It uses data attributes (`data-target-cell`, `data-target-annotation`, `data-code-lines`) to identify the target code cell and annotation, then calculates the position and height of the lines to be highlighted. It dynamically creates or updates a `div` element to visually mark these lines.

```javascript
let selectedAnnoteEl;
const selectorForAnnotation = ( cell, annotation) => {
  let cellAttr = 'data-code-cell="' + cell + '"';
  let lineAttr = 'data-code-annotation="' + annotation + '"';
  const selector = 'span\[' + cellAttr + '\]\[' + lineAttr + '\]';
  return selector;
}

const selectCodeLines = (annoteEl) => {
  const doc = window.document;
  const targetCell = annoteEl.getAttribute("data-target-cell");
  const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
  const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
  const lines = annoteSpan.getAttribute("data-code-lines").split(",");
  const lineIds = lines.map((line) => {
    return targetCell + "-" + line;
  });
  let top = null;
  let height = null;
  let parent = null;

  if (lineIds.length > 0) {
    //compute the position of the single el (top and bottom and make a div)
    const el = window.document.getElementById(lineIds[0]);
    top = el.offsetTop;
    height = el.offsetHeight;
    parent = el.parentElement.parentElement;

    if (lineIds.length > 1) {
      const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
      const bottom = lastEl.offsetTop + lastEl.offsetHeight;
      height = bottom - top;
    }

    if (top !== null && height !== null && parent !== null) {
      // cook up a div (if necessary) and position it
      let div = window.document.getElementById("code-annotation-line-highlight");
      if (div === null) {
        div = window.document.createElement("div");
        div.setAttribute("id", "code-annotation-line-highlight");
        div.style.position = 'absolute';
        parent.appendChild(div);
      }
      div.style.top = top - 2 + "px";
      div.style.height = height + 4 + "px";
      div.style.left = 0;

      let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
      if (gutterDiv === null) {
        gutterDiv = window.document.createElement("div");
        gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
        gutterDiv.style.position = 'absolute';
        const codeCell = window.document.getElementById(targetCell);
        const gutter = code
```

--------------------------------

### Merge Datasets and Summarize Treatment Effects in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/ddd_csdid.ipynb

This snippet demonstrates merging two datasets (f1 and f2) in Stata and then summarizing the 'ATT' variable from both datasets after the merge. It assumes 'f1' is the master dataset and 'f2' is the using dataset, merged on a common key. The `sum` command provides basic statistics for the merged treatment effect variables.

```Stata
use f1, clear
merge 1:1 countyreal using f2
sum ATT_g1 ATT_g2
```

--------------------------------

### Aggregate CSDID Results using csdid_stats in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/ddd_csdid.ipynb

This code demonstrates how to load the previously saved CSDID results ('f1.dta' and 'f2.dta'), use the 'csdid_stats' command to calculate aggregated statistics, rename the ATT variable to distinguish between groups, and then save the aggregated results back to their respective files.

```stata
use f1, clear
csdid_stats simple, save
ren ATT ATT_g1
save f1, replace

use f2, clear
csdid_stats simple, save
ren ATT ATT_g2
save f2, replace
```

--------------------------------

### Estimate Quantile Regressions with Method of Moments (mmqreg)

Source: https://context7.com/friosavila/stpackages/llms.txt

The mmqreg command estimates quantile regressions using the method of moments. It supports multiple fixed effects absorption and joint estimation of multiple quantiles. Dependencies include ftools and hdfe packages.

```stata
* Load NLS data
webuse nlswork, clear

* Install required packages
ssc install ftools
ssc install hdfe

* Median regression with individual fixed effects
mmqreg ln_wage age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure \
    c.tenure#c.tenure not_smsa south, abs(idcode)

* Multiple quantiles (25th and 75th) with fixed effects
mmqreg ln_wage age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure \
    c.tenure#c.tenure not_smsa south, abs(idcode) q(25 75)

* Compare with and without fixed effects
use http://fmwww.bc.edu/RePEc/bocode/o/oaxaca.dta, clear

mmqreg lnwage i.female educ exper tenure i.isco, q(25 75)
mmqreg lnwage i.female educ exper tenure, q(25 75) abs(isco)
mmqreg lnwage educ exper tenure, q(25 75) abs(isco female)

* Robust standard errors
mmqreg lnwage educ exper tenure, q(25 50 75) robust

* Clustered standard errors
mmqreg lnwage educ exper tenure, q(25 50 75) cluster(isco)
```

--------------------------------

### Calculate Percentiles for Confidence Interval Construction - Python

Source: https://github.com/friosavila/stpackages/blob/main/uci/CUI_stata.ipynb

This Python command calculates descriptive statistics for the 'tmax' variable, including percentiles. The output is used to determine the 95th percentile, which serves as the critical t-value for constructing a uniform confidence interval.

```python
frame scu:sum tmax,d
```

--------------------------------

### Save estat Results to a File

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Saves the results of an estat aggregation in Stata to a specified file ('filename') as a '.ster' file. This allows the results to be loaded and used at a later point, facilitating reproducibility and data management.

```Stata
estat [aggregation], esave(filename)
```

--------------------------------

### Plot Quantile Regression Coefficients (qregplot)

Source: https://context7.com/friosavila/stpackages/llms.txt

The qregplot command generates plots of quantile regression coefficients across quantiles. It is compatible with various quantile regression commands including qreg, bsqreg, sqreg, mmqreg, and rifhdreg. It offers extensive customization options for publication-quality graphs.

```stata
* Load data
webuse womenwk, clear

* Estimate base quantile regression
qreg wage age education i.married children i.county

* Plot all coefficients across quantiles 5-95
qregplot age education i.married children, q(5(2.5)95) estore(qp)

* Add OLS comparison line
qregplot age education i.married children, q(5(2.5)95) ols

* Customize confidence interval appearance
qregplot age education i.married children, q(5(2.5)95) ols raopt(color(black%5))

* Single column layout for combined graph
qregplot age education i.married children, q(5(2.5)95) ols col(1) ysize(20) xsize(8)

* Plot from stored estimates
qregplot age education children, from(qp)

* Use variable labels as titles
qregplot age education children, from(qp) label

* Custom titles for subplots
qregplot age education children, from(qp) label \
    mtitles("Age in years" "Years of education")

* With bootstrap standard errors
bsqreg wage age education i.married children i.county
qregplot age education i.married children, q(5(5)95)

* With unconditional quantile regression
rifhdreg wage age education i.married children i.county, rif(q(50))
qregplot age education i.married children, q(5(5)95)

* With method of moments quantile regression
mmqreg wage age education i.married children
qregplot age education i.married children, q(5(5)95)
```

--------------------------------

### jwdid_estat simple: Average Treatment Effect for All Treated

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

The `estat simple` command calculates the average treatment effect on the treated for all observations that were treated at any point. The condition R is defined to include observations treated at or after their group's treatment time.

```stata
jwdid_estat simple
```

--------------------------------

### Store estat Results in Named Estimation Set

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Stores the results from an estat aggregation in Stata under a specified name ('name') in memory. This is analogous to using 'estimation store name' after a regression command and does not overwrite previously estimated results from the 'jwdid' command.

```Stata
estat [aggregation], estore(name)
```

--------------------------------

### Compare Treatment Effects and Test for Differences in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/ddd_csdid.ipynb

This snippet uses the `csdid_rif` command to generate a table comparing two treatment effect estimates ('ATT_g1' and 'ATT_g2'). It then uses the `test` command to perform a hypothesis test checking if the two treatment effects are statistically equal. This is useful for assessing the robustness of the estimated treatment effect.

```Stata
csdid_rif ATT_g1 ATT_g2
test ATT_g1= ATT_g2
```

--------------------------------

### Include High-Order Fixed Effects (Stata)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

This option allows for the inclusion of fixed effects beyond individual and time dimensions, useful for advanced modeling. It is only compatible with 'reghdfe' or 'ppmlhdfe' estimator methods and does not introduce further interactions for these fixed effects.

```stata
reghdfe y x1 x2, fe(fe1 fe2) absorb(fe3)
```

```stata
ppmlhdfe y x1 x2, fe(fe1 fe2) absorb(fe3)
```

--------------------------------

### Calculate Aggregated Average Treatment Effect on the Treated (ATT)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

This snippet outlines the general formula for calculating aggregated average treatment effects on the treated (AGGTE). It involves predicting outcomes under observed covariates and a counterfactual scenario of no treatment, then aggregating these differences based on specific conditions and weights.

```mathematica
AGGTE_r =  ( Sum[ATT_i * w_{i,t} * R_{i,t}, {i, N}] ) / ( Sum[w_{i,t} * R_{i,t}, {i, N}] )
```

--------------------------------

### Estimate DID Model with Pre-Treatment Effects using jwdid

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Estimates a Difference-in-Differences (DID) model that allows for flexible pre-treatment effects using the `jwdid` command with the `never` option. This specification is useful for testing the parallel trends assumption and estimating treatment effects across all relative periods. It includes individual and time fixed effects.

```stata
jwdid y, ivar(i) tvar(t) gvar(g) never
```

--------------------------------

### JavaScript: Select and Unselect Code Lines with Annotations

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This JavaScript function selects and highlights code lines, displaying associated annotations. It manages the visual state of highlighted lines and their corresponding gutter elements. It also handles unselecting lines and removing associated elements.

```javascript
const selectCodeLines = (annoteEl) => {
  const elRect = annoteEl.getBoundingClientRect();
  const top = elRect.top;
  const height = elRect.height;
  let selectedAnnoteEl = undefined;

  const gutter = window.document.querySelector('.code-annotation-gutter');
  if (!gutter) {
    return;
  }

  const gutterDiv = window.document.createElement('div');
  gutterDiv.id = 'code-annotation-line-highlight-gutter';
  gutterDiv.style.top = top - 2 + "px";
  gutterDiv.style.height = height + 4 + "px";
  gutter.appendChild(gutterDiv);

  selectedAnnoteEl = annoteEl;
};

const unselectCodeLines = () => {
  const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
  elementsIds.forEach((elId) => {
    const div = window.document.getElementById(elId);
    if (div) {
      div.remove();
    }
  });
  selectedAnnoteEl = undefined;
};
```

--------------------------------

### Custom Weights for Aggregated ATTs (estat, pw=weight)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

Allows users to specify custom weights for the estimation of aggregated ATTs. The default uses weights from the model estimation. The syntax is `estat [aggregation] [pw = weight]`, where 'weight' is the variable containing the desired weights.

```stata
estat [aggregation], pw(weight_variable)
```

--------------------------------

### JavaScript: Event Listener for Code Annotation Clicks

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This script attaches click event listeners to elements that trigger code annotations. When an annotation element is clicked, it handles selecting or unselecting the code lines and updating the active state of the clicked element.

```javascript
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
for (const annoteDlNode of annoteDls) {
  annoteDlNode.addEventListener('click', (event) => {
    const clickedEl = event.target;
    if (clickedEl !== selectedAnnoteEl) {
      unselectCodeLines();
      const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
      if (activeEl) {
        activeEl.classList.remove('code-annotation-active');
      }
      selectCodeLines(clickedEl);
      clickedEl.classList.add('code-annotation-active');
    } else {
      unselectCodeLines();
      clickedEl.classList.remove('code-annotation-active');
    }
  });
}
```

--------------------------------

### Parallel Trends Test using estat event

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

When using `estat event` with the `never` option, the `pretrend` option can be used to conduct a parallel trends test. This tests the null hypothesis that aggregated treatment effects are zero for all periods before treatment.

```stata
jwdid_estat event, pretrend
```

--------------------------------

### jwdid_estat time: Average Treatment Effect at Specific Times

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

The `estat time` command calculates the average treatment effect at a specific time (t_c) for observations treated at that point. It estimates this for all relevant time periods with treated units.

```stata
jwdid_estat time
```

--------------------------------

### Plot estat Aggregation Results (Twoway Options)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Enables the use of most standard Stata 'twoway' graph options directly with the 'estat plot' command. This provides extensive flexibility in customizing the appearance of the generated plots for aggregated effects.

```Stata
estat plot, twoway_options
```

--------------------------------

### Create Joy Plots and Violin Plots with joy_plot in Stata

Source: https://context7.com/friosavila/stpackages/llms.txt

The `joy_plot` command in Stata generates joy plots (ridge plots) and violin plots to visualize data distributions across different groups. It allows for customization of colors, legends, and the display of interquartile range markers. The command can also perform two-way comparisons and overlay plots.

```stata
* Load data
use http://fmwww.bc.edu/RePEc/bocode/o/oaxaca.dta, clear

* Create grouping variable
gen mstatus = single + 2*married + 3*divorced
label define mstatus 1 "Single" 2 "Married" 3 "Divorced"
label values mstatus mstatus

* Basic joy plot
joy_plot lnwage, over(mstatus)

* With legend instead of text labels
joy_plot lnwage, over(mstatus) alegend notext

* Custom colors
joy_plot lnwage, over(mstatus) alegend color(navy gold)

* Using colorpalette
joy_plot lnwage, over(mstatus) alegend colorpalette(blues) notext

* With interquartile range markers
joy_plot lnwage, over(mstatus) iqr right \
    title("Wages distribution") subtitle("by Marital Status")

* Restricted range
joy_plot lnwage, over(mstatus) iqr range(2 5) \
    title("Wages distribution") subtitle("by Marital Status")

* Two-way comparison: gender within marital status
joy_plot lnwage, over(mstatus) by(female) \
    title("Wages distribution") subtitle("by Marital Status")

* Violin plot
joy_plot lnwage, over(mstatus) by(female) \
    title("Wages distribution") subtitle("by Marital Status and Gender") violin

* Overlay plots by marital status within gender
joy_plot lnwage, by(mstatus) over(female) \
    title("Wages distribution") subtitle("by Marital Status and Gender")
```

--------------------------------

### Manage csdid2 Data in Memory and Disk in Stata

Source: https://github.com/friosavila/stpackages/blob/main/csdid2/README.md

This snippet shows how to clear data currently held in memory by csdid2, save the analysis results to disk for later use, and then load them back into memory.

```stata
csdid2 , clear

csdid2 save_ex1, save 
clear all
csdid2 save_ex1, load
```

--------------------------------

### Estimate Baseline DID Model with Panel Data using jwdid

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Estimates the baseline Difference-in-Differences (DID) model for panel data using the `jwdid` command. It requires specifying the dependent variable, individual identifier, time identifier, and group identifier. Assumes clustered standard errors at the individual level by default.

```stata
jwdid y, ivar(i) tvar(t) gvar(g)
```

--------------------------------

### Estimate Unconditional Standard Errors with estat

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/readme.md

Enables the estimation of unconditional standard errors for aggregated ATTEs in Stata, as suggested by Wooldridge (2021). This requires the underlying command to support Score calculations, such as when 'method(regress)' is used. It is not compatible with 'reghdfe' or 'ppmlhdfe' default methodologies.

```Stata
estat [aggregation], [vce(unconditional)]
```

--------------------------------

### Specify Treatment Heterogeneity Type in jwdid (Stata)

Source: https://github.com/friosavila/stpackages/blob/main/jwdid/JWDID.html

This command shows how to specify different types of treatment effect heterogeneity in the jwdid Stata command. Options include 'time', 'cohort', or 'event' to model how treatment effects vary.

```stata
jwdid y, ivar(i) tvar(t) gvar(g) hettype(option)
```