• High-dimensional fixed effects (HDFE) StataNow
• Bayesian variable selection for linear model StataNow
• Interval-censored multiple-event Cox model StataNow
• Meta-analysis for correlations StataNow
• Correlated random-effects (CRE) model StataNow
• Control-function linear and probit models StataNow
• Bayesian quantile regression StataNow
• Bayesian asymmetric Laplace model StataNow
• Panel-data VAR model StataNow
• Inference robust to weak instruments in instrumental-variables regression StataNow
• SVAR models via instrumental variables StataNow
• Instrumental-variables local-projection IRFs StataNow
• Mundlak specification test StataNow
• Enhancements to teffects StataNow
  • The teffects aipw command for estimating treatment effects via augmented inverse-probability weighting can now provide estimates of the average treatment effects on the treated and can adjust results for sampling weights.
• Robust SEs for VAR models StataNow
  • The var command for fitting vector autoregressive (VAR) models now allows the vce(robust) option to estimate robust standard errors.
• New priors for Bayesian analysis StataNow
  • The bayesmh command and bayes prefix now support the half-Cauchy prior. This heavy-tailed prior is useful for modeling nonnegative parameters such as variances and standard deviations that tend to have large values.
  • The bayesmh command and bayes prefix now support the Rayleigh prior. This prior distribution is related to \(\chi^2\) and exponential distributions and thus can be used to model nonnegative parameters with skewed distributions. It is often used in physics and engineering to model parameters—for instance, wind speed—that correspond to a norm of a bivariate standard normal random vector.
• Gibbs sampling for normal linear models with Laplace priors StataNow
  • The bayesmh command with a univariate normal likelihood and Laplace prior for regression coefficients or with a mean parameter of a normal prior and a Laplace hyperprior now supports Gibbs sampling via the gibbs suboption of the block() option.
• Enhancements to survival graphs StataNow
  • estat gofplot has new options for customizing goodness-of-fit plots. The new plot#opts() and byplot#opts() options control the rendition of plots. After stmgintcox, the new events() and sepevents options produce event-specific cumulative hazard functions; the new event#opts() option controls the rendition of event-specific plots; and the new graph#opts() and by#opts() options control the look of the #th graph.
  • stcurve has new options for customizing graphs of survivor and related functions. The new plot#opts() and atplot#opts() options control the rendition of plots. After stmgitncox, the new events() and sepevents options produce event-specific functions; the new event#opts() option controls the rendition of event-specific plots; the new graph#opts() option controls the look of the #th graph; and the new byopts() option controls how subgraphs are combined and labeled.
• Cumulative structural impulse–response functions (IRFs) StataNow
  • Cumulative structural IRFs are now computed by irf create after ivsvar and ivlpirf. Cumulative structural IRFs sum individual IRFs and are useful for evaluating the total effect of a shock over time. You can graph these statistics with irf graph csirf and tabulate them with irf table csirf.
• Do-file Editor enhancements StataNow
  The Do-file Editor has the following new features:
  • Autocomplete variable names, macros, and stored results. If you pause briefly as you type, the Do-file Editor will now suggest a list of variable names of data in memory, macros, and stored results in addition to the previously suggested commands and words that are already in the do-file. Once the suggestions appear, more typing will narrow down the possibilities. You can navigate the suggestions by using the up- and down-arrow keys or keep typing to narrow them to a single word. Once you have the word you like, you can press Return to place the word in your do-file.
  • Templates. You can now save time and ensure consistency when you create new documents in the Do-file Editor by using Stata templates and user-defined templates. You can access templates by using the New > Document from template menu. Selecting an item from the templates menu will open an editor and set the contents of the editor to the contents of the template. You can define your own templates by creating the document_templates directory in your PERSONAL directory and then saving template files into that directory.
  • Current word and selection highlighting. Stata’s Do-file Editor will highlight all case-insensitive occurrences of the current word under the cursor. It will also separately highlight all case-sensitive occurrences of the current selection. Both features allow you to easily find keyword or selection matches in your document without having to use the Find dialog. Of course, either feature or both can be disabled from the Preferences dialog.
  • Code folding enhancements. Code folding allows you to selectively hide parts of a document so that you can focus on sections of interest. Stata’s Do-file Editor allows you to selectively fold blocks of code in a do-file such as programs, Mata code, Python code, functions, and if statements by collapsing them to a single line. You can now quickly fold all foldable blocks of code in your do-file by using the Fold all menu item. You can then selectively unfold your code one fold point at a time to show the more important parts of your do-file, or you can use the Do-file Editor’s Unfold all menu item to unfold every fold point. You can also select lines of code and transform them into a foldable block of code by using the Fold selection menu item. This can tidy up your code and increase the code’s readability. And finally, there is a new setting for the Do-file Editor that will automatically fold every foldable block of code of a do-file when it is opened.
  • Temporary and permanent bookmarks. Stata's Do-file Editor now supports temporary bookmarks in addition to the existing permanent bookmarks. Temporary bookmarks allow you to easily navigate through your do-file without making any permanent changes to its content. These bookmarks can be added to any line of your do-file that does not already have a permanent bookmark and will be removed when your do-file is closed.
• Colors by variable for more graphs StataNow
  • The colorvar() option is now available with additional two-way plots: line, connected, tsline, rline, rconnected, and tsrline. This means you can vary color lines, markers, and more in these plots based on the values of a specified variable.
• PyStata enhancements StataNow
  The PyStata features for integrating Python into Stata and integrating Stata into Python have the following improvements:
  • When running Stata code within an IPython kernel-based environment, such as Jupyter Notebook and console as well as Jupyter Lab and console, and within other environments that support the IPython kernel, such as Spyder IDE and PyCharm IDE, Stata’s variable names in the current working dataset, macro names, and results r(), e(), and s() can now be autocompleted as you type by pressing the Tab key.
  • A new %help line magic is now available; it allows you to view the help information of the specified Stata command or topic in the web browser.
  • You can now control whether to echo the Stata commands along with their output when executing them in the Python environment; and you can control whether to display Stata’s output simultaneously when the execution begins or to display the output after Stata finishes execution.
  • In the sfi module, new class BreakError is available; it allows for interrupting Python execution by using the Break key in Stata.
• Enhancements for listing data StataNow
  • The list command has two new options:
  1. sepbyexp(exp) draws a separator line whenever the value of expression exp changes. exp does not necessarily have to refer to the variables in the dataset;
  2. footer displays variable names as a footer.

• Bayesian model averaging (BMA)
• Bayesian variable selection for linear model StataNow
• Bayesian quantile regression StataNow
• Bayesian asymmetric Laplace model StataNow
• New priors for Bayesian analysis StataNow
  • The bayesmh command and bayes prefix now support the half-Cauchy prior. This heavy-tailed prior is useful for modeling nonnegative parameters such as variances and standard deviations that tend to have large values.
  • The bayesmh command and bayes prefix now support the Rayleigh prior. This prior distribution is related to \(\chi^2\) and exponential distributions and thus can be used to model nonnegative parameters with skewed distributions. It is often used in physics and engineering to model parameters—for instance, wind speed—that correspond to a norm of a bivariate standard normal random vector.
• Gibbs sampling for normal linear models with Laplace priors StataNow
  • The bayesmh command with a univariate normal likelihood and Laplace prior for regression coefficients or with a mean parameter of a normal prior and a Laplace hyperprior now supports Gibbs sampling via the gibbs suboption of the block() option.

• Causal mediation analysis
• Heterogeneous DID
• After fitting a difference-in-differences model with didregress or xtdidregress to data comprising multiple cohorts that are treated at different times, you can use the new estat bdecomp command to decompose the average treatment effect on the treated (ATET) into components. The results are useful in determining whether the treatment effects are heterogeneous and, if so, how much the heterogeneity impacts the overall ATET reported by didregress or xtdidregress.
• Enhancements to teffects StataNow
  • The teffects aipw command for estimating treatment effects via augmented inverse-probability weighting can now provide estimates of the ATETs and can adjust results for sampling weights.

Additional updates

• When you estimate treatment effects with telasso and use a Poisson model for your outcome, you can now specify an exposure variable that reflects the amount of time over which events were observed.
• When you fit a heterogeneous difference-in-differences model with xthdidregress or hdidregress, you can use the new option basetime(), which determines the base time to be used for the pretreatment periods. The basetime(adaptive) option specifies that the base time is adaptive, meaning that for each pretreatment period, the base time is the previous pretreatment period for that cohort. Alternatively, you can specify the basetime(common) option to use the last pretreatment period as the common base time for all pretreatment periods for the cohort. The common base time is particularly useful when evaluating whether the assumption of parallel trends is violated in event studies.
• You can now use the eform option to request that exponentiated coefficients be reported when fitting a model with eregress or xteregress. Exponentiated coefficients are useful, for example, when interpreting results of a linear model fit to a log-transformed outcome.

• Alias variables across frames
• You can use the new export spss command to export a Stata dataset to an SPSS (.sav) file.
• Enhancements for listing data StataNow
• The list command has two new options:
1. sepbyexp(exp) draws a separator line whenever the value of expression exp changes. exp does not necessarily have to refer to the variables in the dataset;
2. footer displays variable names as a footer.
• The import delimited command has a new emptylines(skip|include) option to control how empty lines are processed as observations.
• The icd10 commands have been updated to include ICD-10 diagnosis codes from the sixth edition (2019).
• The contract command is now faster.

Additional updates

• reshape is now faster—often 10 times, 100 times, or even more times faster. With the new favor(speed) option of reshape, you can reshape your data from long form to wide form or from wide form to long form in a manner that is faster but may require more memory than when you use the default favor(memory) option.
• Alternatively, you may type set reshape_favor speed to specify that the default method for reshaping favors speed.
• The new import haverdirect command allows you to import economic and financial data from Haver Analytics databases with the use of Haver's Cloud platform.
• The import haver command has the following improvements:
• import haver can now now import 7-daily series from Haver Analytics databases.
• import haver has a new frame() option that allows series information to be saved to a specified data frame.
• The jdbc connect and jdbc add commands now support usernames and passwords greater than 5,000 characters.
• The icd10cm and icd10pcs commands for working with ICD-10-CM diagnosis and procedure codes have been updated for the 2025 fiscal year.

• Boost-based regular expressions
• Seven new datetime functions, mostly related to days of the week, are now available. Read the overview.

• Tables of descriptive statistics
• Robust inference for linear models
• Wild cluster bootstrap
• High-dimensional fixed effects (HDFE) StataNow
• Control-function linear and probit models StataNow
• Flexible demand systems
• RERI
• Instrumental-variables quantile regression
• Instrumental-variables fractional probit model
• Inference robust to weak instruments StataNow
• New spline functions
• Many Stata estimation commands support the vce(robust) option for estimating robust standard errors and the vce(cluster clustvar) option for estimating cluster–robust standard errors. These options are now supported by two additional commands:
• The sureg command, which fits seemingly unrelated regression models.
• The reg3 command, which fits systems of simultaneous equations via three-stage least squares.
• Exact p-values are now available for Spearman's rank correlation coefficients. The spearman command now supports the exact() option to compute the exact p-value using a Monte Carlo sampling of the permutation distribution or using a complete enumeration of the permutation distribution.
• Stata's factor-variable notation allows users to specify categorical variables and interactions in variable lists in many commands. This notation is now supported by additional commands:
• The exlogistic command, which fits exact logistic regression models.
• The expoisson command, which fits exact Poisson regression models.
• After fitting simultaneous-quantile regression models with sqreg, you can use the new estat coefplot command to plot the coefficients and their confidence intervals across quantiles.
• The nlcom command, which computes nonlinear combinations of parameters, now supports the eform[()] option to report exponentiated nonlinear parameters.
• The table command now computes two additional statistics: the geometric mean and the geometric standard deviation, which are specified using statistic(geomean) and statistic(geosd), respectively. In addition, strL variables may now be used to define the rows, columns, and separate tables.

Additional updates

• When computing numerical derivatives with ml model, you can now specify a minimum step size, which is helpful for avoiding unstable results that can arise during iterative maximization when the computed step size is too small.

• All-new graph style
• Graph colors by variable
• Colors by variable for more graphs StataNow
  • The colorvar() option is now available with additional two-way plots: line, connected, tsline, rline, rconnected, and tsrline. This means you can vary color lines, markers, and more in these plots based on the values of a specified variable.
• When you create by-graphs, you can now specify the altleg bystyle to move the legend to the six o'clock position and use two columns for the legend. For instance, specifying the by(group, style(altleg)) option will create the graph for each value of group and will place the two-column legend at the bottom of these graphs.
• You can now specify a minimum length for the axis labels with the labelminlen(#) option. This can be particularly helpful if you are creating multiple graphs that you intend to combine by using graph combine. You can, for instance, specify ylabel(labelminlen(5)) with each graph to ensure that at least five characters are used for the y-axis labels; labels are padded with spaces on the left if needed. Adding this option to each graph specification will allow you to create graphs with labels of the same width so that axes will align nicely when combined.

Additional updates

• When exporting a graph to a PDF, you can now customize the font, width, height, and other aspects of the PDF.
• We introduced an all-new graph style with a new color palette, horizontal y-axis labels, legend on the right side of the graph, and more. These new features are available in the st family of schemes.

  For those who prefer a monochrome graph, we now offer two additional schemes: stmono1 and stmono2. These schemes follow the look of the labels, legends, and other features of the st schemes but with monochromatic markers and fill colors.

  For those publishing articles in the Stata Journal, the new stsj scheme reflects the style of the st family with horizontal y-axis labels and a white background. This is now the official scheme for the Stata Journal.

• graph twoway now allows you to specify aspect ratios based on common units in the x and y dimensions. Adding the aspectratio(1, units) option creates a graph where one unit in the x dimension and one unit in the y dimension take up the same distance on the plot. This is useful for plotting things such as latitude against longitude, which have common scaling of their units.

  You can also specify arbitrary relative scales—typing aspectratio(100, units) specifies that each unit in the y dimension is 100 times the length of a unit in the x dimension.

• You can now use OpenType fonts (.otf) in graphs when you use graph export to export your graph to SVG, PDF, PNG, JPEG, EMF, TIF, or GIF.
• When you use graph export to export a graph to an SVG file, you can use the new pre() option to specify how sequences of spaces in text elements should be handled. By default, browsers and applications that support SVG files will collapse sequences of whitespace in a text element into a single whitespace. Specifying pre(on) will set the style for text elements to preserve sequences of whitespace. The default, pre(off), will take no action on text elements that contain sequences of whitespace.

• The Do-file Editor now allows automatic backups and syntax highlighting of user-defined keywords.
• The Data Editor now has pinnable rows and columns, resizable cell editors, tooltips for truncated text, the ability to show variable labels in the column headers, and proportional-width fonts.
• Do-file Editor enhancements StataNow
  The Do-file Editor has the following new features:
  • Autocomplete variable names, macros, and stored results. If you pause briefly as you type, the Do-file Editor will now suggest a list of variable names of data in memory, macros, and stored results in addition to the previously suggested commands and words that are already in the do-file. Once the suggestions appear, more typing will narrow down the possibilities. You can navigate the suggestions by using the up- and down-arrow keys or keep typing to narrow them to a single word. Once you have the word you like, you can press Return to place the word in your do-file.
  • Templates. You can now save time and ensure consistency when you create new documents in the Do-file Editor by using Stata templates and user-defined templates. You can access templates by using the New > Document from template menu. Selecting an item from the templates menu will open an editor and set the contents of the editor to the contents of the template. You can define your own templates by creating the document_templates directory in your PERSONAL directory and then saving template files into that directory.
  • Current word and selection highlighting. Stata’s Do-file Editor will highlight all case-insensitive occurrences of the current word under the cursor. It will also separately highlight all case-sensitive occurrences of the current selection. Both features allow you to easily find keyword or selection matches in your document without having to use the Find dialog. Of course, either feature or both can be disabled from the Preferences dialog.
  • Code folding enhancements. Code folding allows you to selectively hide parts of a document so that you can focus on sections of interest. Stata’s Do-file Editor allows you to selectively fold blocks of code in a do-file such as programs, Mata code, Python code, functions, and if statements by collapsing them to a single line. You can now quickly fold all foldable blocks of code in your do-file by using the Fold all menu item. You can then selectively unfold your code one fold point at a time to show the more important parts of your do-file, or you can use the Do-file Editor’s Unfold all menu item to unfold every fold point. You can also select lines of code and transform them into a foldable block of code by using the Fold selection menu item. This can tidy up your code and increase the code’s readability. And finally, there is a new setting for the Do-file Editor that will automatically fold every foldable block of code of a do-file when it is opened.
  • Temporary and permanent bookmarks. Stata's Do-file Editor now supports temporary bookmarks in addition to the existing permanent bookmarks. Temporary bookmarks allow you to easily navigate through your do-file without making any permanent changes to its content. These bookmarks can be added to any line of your do-file that does not already have a permanent bookmark and will be removed when your do-file is closed.
• jdbc load now has a dialog that allows you to
• add or remove data source names (DSNs),
• filter tables of a DSN or filter columns of a table, and
• select which columns of a table to load into Stata.
• When you run multiple instances of Stata in Windows, the Stata instance number will now appear on the following top-level windows: Do-File Editor, Data Editor, Variables Manager, SEM Builder, Graph windows, and Viewer windows.
• In Windows, the new set taskbargroups setting affects how Stata windows are grouped on the taskbar. If taskbar grouping is enabled, different instances of Stata will be grouped separately on the taskbar. This setting is enabled by default.
• Stata for Mac now prompts you for your preferred window layout when it is first launched. You can choose the Sidebar layout, which may be preferable for small laptop displays, or the Widescreen layout, which may be preferable for desktop monitors or large laptop displays.

Additional updates

• The Do-file Editor now extends syntax highlighting of comments to lines joined by ///.
• The Data Editor now supports left- or right-aligning text based on the justification from a variable's format. The setting can be enabled from the Data Editor's preferences.
• The doedit command, which opens the Do-file Editor, can now open URLs.

• Instrumental-variables quantile regression
• Instrumental-variables fractional probit model
• Inference robust to weak instruments StataNow
• SVAR models via instrumental variables StataNow
• Instrumental-variables local-projection IRFs StataNow
• Control-function linear and probit models StataNow

• Lasso for Cox model

Additional updates

• When you estimate treatment effects with telasso and use a Poisson model for your outcome, you can now specify an exposure variable that reflects the amount of time over which events were observed.

• Heterogeneous DID
• High-dimensional fixed effects (HDFE) StataNow
• Correlated random-effects (CRE) model StataNow
• Control-function linear model StataNow
• Robust inference for linear models
• Wild cluster bootstrap
• Instrumental-variables quantile regression
• Inference robust to weak instruments StataNow
• Mundlak specification test StataNow
• Panel-data VAR model StataNow
• Bayesian quantile regression StataNow
• Many Stata estimation commands support the vce(robust) option for estimating robust standard errors and the vce(cluster clustvar) option for estimating cluster–robust standard errors. These options are now supported by two additional commands:
• The sureg command, which fits seemingly unrelated regression models.
• The reg3 command, which fits systems of simultaneous equations via three-stage least squares.
• After fitting a difference-in-differences model with didregress or xtdidregress to data comprising multiple cohorts that are treated at different times, you can use the new estat bdecomp command to decompose the average treatment effect on the treated (ATET) into components. The results are useful in determining whether the treatment effects are heterogeneous and, if so, how much the heterogeneity impacts the overall ATET reported by didregress or xtdidregress.
• After fitting simultaneous-quantile regression models with sqreg, you can use the new estat coefplot command to plot the coefficients and their confidence intervals across quantiles.

Additional updates

• You can now use the eform option to request that exponentiated coefficients be reported when fitting a model with cnsreg, eivreg, eregress, hetregress, intreg, rreg, tobit, truncreg, or xteregress. Exponentiated coefficients are useful, for example, when interpreting results of a linear model fit to a log-transformed outcome.

• Vectorized numerical integration
• The Mata xl() class includes seven new member functions:
• put_string() writes a string scalar, vector, or matrix to a Microsoft Excel worksheet.
• set_header() adds a worksheet header to the active worksheet.
• set_footer() adds a worksheet footer to the active worksheet.
• insert_horizontal_page_break() inserts a page break at the specified row in the active worksheet.
• delete_horizontal_page_break() deletes a page break at the specified row in the active worksheet.
• insert_vertical_page_break() inserts a page break at the specified column in the active worksheet.
• delete_vertical_page_break() deletes a page break at the specified column in the active worksheet.

Additional updates

• When computing numerical derivatives with Mata functions deriv(), moptimize(), optimize(), and solvenl(), you can now specify a minimum step size, which is helpful for avoiding unstable results that can arise during iterative maximization when the computed step size is too small.
• You can now use the complex step method when you compute numerical derivatives using Mata's deriv() function.

• Multilevel meta-analysis
• Meta-analysis for prevalence
• Meta-analysis for correlations StataNow
• After meta regress, you can now use predict with the reses() option, which is specified with the reffects option, to compute comparative standard errors for the random effects. Diagnostic standard errors can be obtained by adding the diagnostic suboption.
• After meta mvregress, you can now use predict with the reses() option to compute comparative standard errors for the random effects by default. Diagnostic standard errors can be obtained by adding the diagnostic suboption.

Additional updates

• Computing marginal means, marginal effects, and other marginal estimates with margins is now much faster (up to 10 times faster) after fitting a nonlinear mixed-effects model with menl.

• Stata's factor-variable notation allows users to specify categorical variables and interactions in variable lists in many commands. This notation is now supported by additional commands:
• The discrim knn command, which performs kth-nearest-neighbor discriminant analysis.
• The discrim logistic command, which performs logistic discriminant analysis.

• Heterogeneous DID
• High-dimensional fixed effects (HDFE) StataNow
• Correlated random-effects (CRE) model StataNow
• Panel-data VAR model StataNow
• Robust inference for linear models
• Wild cluster bootstrap
• Mundlak specification test StataNow

Additional updates

• You can now use the eform option to request that exponentiated coefficients be reported when fitting a model with xtreg or xteregress. Exponentiated coefficients are useful, for example, when interpreting results of a linear model fit to a log-transformed outcome.

• Group sequential designs

• PyStata enhancements StataNow
  The PyStata features for integrating Python into Stata and integrating Stata into Python have the following improvements:
  • When running Stata code within an IPython kernel-based environment, such as Jupyter Notebook and console as well as Jupyter Lab and console, and within other environments that support the IPython kernel, such as Spyder IDE and PyCharm IDE, Stata’s variable names in the current working dataset, macro names, and results r(), e(), and s() can now be autocompleted as you type by pressing the Tab key.
  • A new %help line magic is now available; it allows you to view the help information of the specified Stata command or topic in the web browser.
  • You can now control whether to echo the Stata commands along with their output when executing them in the Python environment; and you can control whether to display Stata’s output simultaneously when the execution begins or to display the output after Stata finishes execution.
  • In the sfi module, new class BreakError is available; it allows for interrupting Python execution by using the Break key in Stata.
• The levelsof command is now faster.
• The Stata–Python API specification has new features and improvements:
• The ValueLabel class can now work with Stata's extended missing values using the following methods:
1. ValueLabel.getLabel(name, value) allows value to be .a, .b, ..., .z in addition to an integer value so that it can return the labels associated with Stata's missing values.
2. ValueLabel.getValueLabels(name) returns Stata's missing label as a key if the value label contains a missing value associated with a label. Previously, the key was returned as an integer missing value.
3. ValueLabel.setLabelValue(name, value, label) allows value to be .a, .b, ..., .z in addition to an integer value so that it can set labels for missing values.
4. ValueLabel.getValues(name) returns Stata's missing label in the result if the value label contains a missing value associated with a label. Previously, the value was returned as an integer missing value.
5. ValueLabel.removeLabelValue(name, value) allows value to be .a, .b, ..., .z in addition to an integer value so that it can remove labels for missing values.
• The Missing class has the following improvements:
  1. Missing.getValue(val=None) allows users to input None or ., .a, ..., .z to access Stata's missing values. Previously, val could be None or a, b, ..., z.
  2. Missing.getMissing(value) gets the missing symbol associated with value that represents the corresponding missing value in Stata.
• The Data class has a new function, isAlias(var), that returns whether a variable in the current dataset is an alias for a variable in another frame.
• The Frame class has a new function, isAlias(var), that returns whether a variable in the current dataset is an alias for a variable in another frame.
• The Stata–Java API Specification has the following new features:
• The ValueLabel class can now work with Stata's extended missing values using the following methods:
1. ValueLabel.getLabel(java.lang.String, double) gets the label for a specified value-label value.
2. ValueLabel.getValueLabels(String name, Map<LabelValue,String> map) gets the value and label pairings for a specified value-label name.
3. ValueLabel.removeLabelValue(String name, Missing.Extended missingValue) removes a value-label value from the specified value-label name.
4. ValueLabel.setLabelValue(String name, Missing.Extended missingValue, String label) sets a value and label for a value-label name.
• The LabelValue class was added to encapsulate a Stata value-label value.
• The Data class has a new function, isAlias(int var), that returns whether a variable in the current dataset is an alias for a variable in another frame.
• The Frame class has a new function, isAlias(int var), that returns whether a variable in the current dataset is an alias for a variable in another frame.
• The matlist command has two new options for customizing the display of matrices. The rightindent option indents data by one space relative to the end of row lines. The rowtitleleft option displays row titles flush left.
• Programmers working with alias variables in frames can take advantage of four new macro functions:
• isalias varname returns 1 for an alias variable and 0 otherwise.
• aliasframe varname returns the name of a frame that varname is linked to.
• aliaslinkname varname returns the name of the linking variable that was used to create varname.
• aliasvarname varname returns the name of the variable that varname is linked to.

Additional updates

• When working with h2o, subcommands of _h2oframe no longer require a leading underscore. For example, _h2oframe put can be used instead of _h2oframe _put.
• h2o init will now generate credentials and create new H2O instances using those credentials by default. The new h2o credentials query and h2o credentials clear commands are available for retrieving and managing credentials that Stata automatically creates using h2o init.
• The _h2oframe command for interacting with H2O has the following new subcommands:
• _h2oframe factor converts columns in the H2O frame to categorical.
• _h2oframe levelsof lists all levels of a categorical column.
• _h2oframe baselevel sets the base level of a categorical column.
• _h2oframe recodelevel assigns new levels of a categorical column.

• Tables of descriptive statistics
• putdocx now allows bookmarks in paragraphs and tables, image text for voice software, and SVG images in Word. Learn more.
• putexcel now allows you to freeze worksheets, add page breaks, include hyperlinks, and insert headers and footers in Excel. Learn more.

Additional updates

• putdocx and putpdf can now create documents including up to 10,000 tables. You can specify the maximum number of tables allowed by using the new set docx_maxtable and set pdf_maxtable commands.
• dtable now allows you to test for differences in continuous variables across groups using the version of the Kruskal–Wallis rank test that adjusts for ties.
• When creating tables with collect, you can now more easily hide the title of a factor variable when you are using the table style that stacks row header elements in one column.
• Tables produced by collect now automate appropriate labeling of t and z statistics in more situations. For instance, when you create a table of linear regression results from the regress command, the test statistics are labeled as “z”. However, when you request bootstrap standard errors, the statistics are now automatically labeled as “t”.
• table now allows you to specify the statistics you would like to report using abbreviations. For instance, you can specify freq rather than frequency when requesting that frequencies be reported.
• You can now use OpenType fonts (.otf) when creating PDF documents with putpdf.
• You can now use OpenType fonts when you use collect export, dtable, or etable to export a table to PDF.
• You now have more control over formats in tables created by dtable and table. With the new basestyle suboption within the nformat() option, you can change the format for results that do not already have one without overriding the format for all results.

• Interval-censored multiple-event Cox model StataNow
• TVCs with interval-censored Cox model
• Lasso for Cox model
• GOF plots for survival models
• RERI
• The stcurve command plots the survivor, failure, hazard, or cumulative hazard function after fitting many models for survival-time data. In Stata 18, stcurve has the following new features:
• After fitting a shared-frailty Cox model with stcox, you can now specify the expression _frailty = (numlist) in the at() option to adjust estimates of survivor and related functions for frailties set to the values in numlist.
• After fitting a Cox model for a multiple-record-per-subject interval-censored dataset using stintcox, you can specify the new atmeans option to evaluate the survivor or other function at time-specific means of the covariate.
• After fitting a Cox model for a multiple-record-per-subject interval-censored dataset using stintcox, you can specify the new atframe(frname) option to evaluate the survivor or other function at the values of the variables specified in the frname frame.
• After lasso cox or elasticnet cox, you can calculate predictions based on penalized coefficients by default, or you can calculate predictions based on postselection coefficients by specifying the postselection option.
• After fitting a shared-frailty Cox model with stcox, predict now allows the atfrailty and atfrailty(varname|#) options when you predict the baseline survivor function, baseline cumulative-hazard function, or baseline hazard contributions. If you specify atfrailty, frailties are set to their estimated values when computing predictions. If you specify atfrailty(varname|#), frailties are instead set to the values in varname or #.
• The stintcox command, which fits Cox proportional hazards models for interval-censored data, now supports the vce(robust) option for estimating robust standard errors and the vce(cluster clustvar) option for estimating cluster–robust standard errors.
• Enhancements to survival graphs StataNow
  • estat gofplot has new options for customizing goodness-of-fit plots. The new plot#opts() and byplot#opts() options control the rendition of plots. After stmgintcox, the new events() and sepevents options produce event-specific cumulative hazard functions; the new event#opts() option controls the rendition of event-specific plots; and the new graph#opts() and by#opts() options control the look of the #th graph.
  • stcurve has new options for customizing graphs of survivor and related functions. The new plot#opts() and atplot#opts() options control the rendition of plots. After stmgitncox, the new events() and sepevents options produce event-specific functions; the new event#opts() option controls the rendition of event-specific plots, the new graph#opts() option controls the look of the #th graph, and the new byopts() option controls how subgraphs are combined and labeled.

• Local projections for IRFs
• SVAR models via instrumental variables StataNow
• Instrumental-variables local-projection IRFs StataNow
• Model selection for ARIMA and ARFIMA
• Robust SEs for VAR models StataNow
  • The var command for fitting vector autoregressive (VAR) models now allows the vce(robust) option to estimate robust standard errors.
• Cumulative structural impulse–response functions (IRFs) StataNow
• Cumulative strucutral IRFs are now computed by irf create after ivsvar and ivlpirf. Cumulative structural IRFs sum individual IRFs and are useful for evaluating the total effect of a shock over time. You can graph these statistics with irf graph csirf and tabulate them with irf table csirf.

• Here we have told you about many of the new features in Stata 18. Yet there is still more. See the What's new for a complete chronological list of updates.