Update for July 2022 release

Author: Symbolics

assets/icons/logo-black.svg

@@ -175,7 +175,7 @@ enable = false
 # End user relevant links. These will show up on left side of footer and in the community page if you have one.
 [[params.links.user]]
 	name = "User mailing list"
-	url = "https://groups.google.com/g/lisp-stat"
+	url = "https://groups.google.com/g/lisp-stat/"
 	icon = "fa fa-envelope"
         desc = "Discussion and help from your fellow users"
 # [[params.links.user]]
@@ -185,14 +185,14 @@ enable = false
 #         desc = "Follow us on Twitter to get the latest news!"
 [[params.links.user]]
 	name = "Stack Overflow"
-	url = "https://stackoverflow.com/questions/tagged/xlispstat"
+	url = "https://stackoverflow.com/questions/tagged/xlispstat/"
 	icon = "fab fa-stack-overflow"
         desc = "Practical questions and curated answers"
 
 # Developer relevant links. These will show up on right side of footer and in the community page if you have one.
 [[params.links.developer]]
 	name = "GitHub"
-	url = "https://github.com/lisp-stat"
+	url = "https://github.com/Lisp-Stat/"
 	icon = "fab fa-github"
         desc = "Development takes place here"
 # [[params.links.developer]]
@@ -202,7 +202,7 @@ enable = false
 #         desc = "Chat with other project developers"
 [[params.links.developer]]
 	name = "Developer discussion"
-	url = "https://github.com/Lisp-Stat/lisp-stat/discussions"
+	url = "https://github.com/Lisp-Stat/lisp-stat/discussions/"
 	icon = "fa fa-envelope"
         desc = "Discuss development issues around the project"
 

config.toml

@@ -21,9 +21,9 @@
 {{% blocks/lead color="primary" %}}
 
 Lisp-Stat provides a statistical computing engine suitable for all
-phases of the machine learning lifecycle. Ideal for AI DevOps,
+phases of the machine learning life-cycle. Ideal for AI DevOps,
 Lisp-Stat covers desktop exploration through to production API
-endpoints. Lisp-Stat provides a secure, safe and managable environment.
+endpoints. Lisp-Stat provides a secure, safe and manageable environment.
 
 
 {{% /blocks/lead %}}
@@ -57,9 +57,16 @@
 {{% blocks/feature icon="fas fa-code" title="Load a R data set" %}}
 <pre style="text-align: left">
   <code>
-(defdf mtcars (read-csv rdata:mtcars))
-
-
+    (data :mtcars)
+    (head mtcars)
+
+;;   MODEL              MPG CYL DISP  HP DRAT    WT  QSEC VS AM GEAR CARB
+;; 0 Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
+;; 1 Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
+;; 2 Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
+;; 3 Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
+;; 4 Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
+;; 5 Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
   </code>
 
 

content/en/_index.html

@@ -9,16 +9,16 @@ layout: docs
 
 {{% blocks/cover title="About Lisp-Stat" height="auto" %}}
 
-Lisp-Stat is an environment for statistical data exploration and deployment of AI and machine learning models
+Lisp-Stat is an environment for statistical computing
 
 
 {{% /blocks/cover %}}
 
 {{% blocks/section type="section" color="primary" %}}
 
 Lisp-Stat is conceptually similar to R and will be familiar to most
-people from that ecosystem.  It is suitable for both exploratory
-analytics as well as front-line production deployments. Common Lisp is
+people from that ecosystem.  It is suitable for both exploratory data
+analysis as well as front-line production deployments.  Common Lisp is
 currently used at Google in several high-availability, high-volume
 transactional systems.
 
@@ -30,8 +30,15 @@ system had to:
 - Be robust enough to work in an enterprise production environment
 - Be available under a license without source code restrictions
 
-Common Lisp was the only framework that met all these
-requirements.
+Common Lisp was the only framework that met all these requirements.
+
+Probably the most important reasons though are given in the paper by
+Ross Ihaka, one of the originators of the R language, [Lisp as a Base
+for a Statistical Computing
+System](https://www.stat.auckland.ac.nz/~ihaka/downloads/Compstat-2008.pdf)
+about the deficiencies in R and the inability to compile to
+machine code (among other issues). The same is true of Python. In that
+paper he argues for Lisp as a replacement for R.
 
 {{% /blocks/section %}}
 
@@ -40,9 +47,9 @@ requirements.
 
 ## What does Lisp-Stat do?
 
-Lisp-Stat provides support for vectorized mathematical operations, and
+Lisp-Stat provides support for vectorized mathematical operations and
 a comprehensive set of statistical methods that are implemented using
-the latest numerical algorithms. In addition, Common Lisp provides a
+the latest numerical algorithms.  In addition, Common Lisp provides a
 dynamic programming environment
 ([REPL](https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop)),
 an excellent object-oriented facility
@@ -52,27 +59,12 @@ and meta-object protocol
 
 Lisp-Stat is functional today and in daily use on several projects. It has an [archive of libraries](https://github.com/Lisp-Stat/xls-archive) from XLISP-STAT that can be used with the aid of a compatibility package ([XLS-compat](https://github.com/Lisp-Stat/XLS-compat)).  The archive includes packages for linear models, KNN, advanced statistics, temporal/spatial reasoning and a Lisp version of the [NSWC Library of Mathematics Subroutines](https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/ADA261511.xhtml). It also includes the [Cephes mathmatical library](https://github.com/Lisp-Stat/cephes.cl) for accurate statistical distribution calculations.
 
-### Data-Frame
-
-Lisp-Stat includes a column-oriented data-frame.   Data may be
-loaded from the network, local disk or a relational database.
-
-### Notebooks
-
-[JupyterLab](http://jupyter.org/), along with
-[common-lisp-jupyter](https://github.com/yitzchak/common-lisp-jupyter/)
-are used to provide notebook style environments for reproducible
-research.
-
-### Visualization
-
-Interactive graphics and plotting use [Vega-Lite](https://vega.github.io/vega-lite/) as a backend.  See [getting started](/docs/getting-started/) or the [plotting examples](/docs/examples/plotting/) for a quick introduction.
-
+For more on what Lisp-Stat can do, see [getting started](/docs/getting-started/) and the [overview](/docs/overview).
 
 {{% /blocks/section %}}
 
-{{% blocks/section type="section" color="primary" %}}
 
+{{% blocks/section type="section" color="primary" %}}
 ## What's next for Lisp-Stat?
 
 Lisp-Stat is an open source project and we welcome patches and

content/en/about/_index.md

@@ -4,6 +4,7 @@ title: "Statistical Analysis with Lisp-Stat"
 linkTitle: "Announcing Lisp-Stat"
 description: "Version 1.0-beta"
 author: Steve Nunez
+vega: true
 resources:
 - src: "**.{png,jpg}"
   title: "Image #:counter"
@@ -34,7 +35,7 @@ system had to:
 
 Common Lisp was the only framework that met all these requirements.
 And, it is not the first time lisp has been used in a statistical
-setting.  [XLISP-STAT](https://en.wikipedia.org/wiki/XLispStat), our
+setting.  [XLISP-STAT](https://en.wikipedia.org/wiki/XLispStat/), our
 spiritual predecessor, was a contemporary of R in the early days of
 development.  Wikipedia says about it: "XLispStat was historically
 influential in the field of statistical visualization" and its author,
@@ -45,7 +46,7 @@ Luke Tierney, was a member of the original R core team.
 
 Lisp-Stat provides support for vectorized mathematical operations, a
 super-set of common lisp [array
-operations](/docs/tasks/array-operations/), and a comprehensive set of
+operations](/docs/manuals/array-operations/), and a comprehensive set of
 statistical methods that are implemented using the latest numerical
 algorithms.  In addition, Common Lisp provides a dynamic programming
 environment that includes a
@@ -64,7 +65,7 @@ the archive.
 ## Getting Started
 
 If you are familiar with R, and want to understand how to do similar
-things with Lisp-Stat, the [basic tutorial](/docs/tutorials/basics) is
+things with Lisp-Stat, the [basic tutorial](/docs/tutorials/basics/) is
 the place to begin.  If you have a Common Lisp installation and want
 give Lisp-Stat a quick try, start with [Install to Plot in 5
 minutes](/docs/getting-started/).  For newcomers to lisp, see the
@@ -79,22 +80,26 @@ All the basics for descriptive statistical analysis.
 Load the classic R `mtcars` data set into a data frame:
 
 ```lisp
-(define-data-frame mtcars
-  (read-csv (rdata 'datasets 'mtcars)))
+(data :mtcars)
 ```
 
 ### Plotting
 
 The [plotting examples](/docs/examples/plotting/) show how to plot the
 horsepower vs. miles-per-gallon of the similar Vega-Lite data set:
 
+```lisp
+(plot:plot
+  (vega:defplot hp-mpg
+  `(:title "Horsepower vs. MPG"
+    :data ,vgcars
+    :mark :point
+	:encoding (:x (:field :horsepower :type :quantitative)
+	           :y (:field :miles-per-gallon :type :quantitative)))))
 ```
-(defparameter cars-scatter-plot
-  (vglt:scatter-plot vega-cars "HORSEPOWER" "MILES_PER_GALLON"))
-(plot:plot-from-file (vglt:save-plot 'cars-scatter-plot))
-```
 
-{{< figure src="/docs/examples/vega-cars-colored-scatter-plot.png" >}}
+{{< vega id="ex-hp-mpg" spec="/plots/hp-mpg.vl.json" >}}
+
 
 ### Jupyter notebooks
 For reproducible research and sharing.
@@ -105,24 +110,24 @@ Lisp-Stat running in a Jupyter-Lab notebook
 
 ### Examples & Tutorials
 
-The [examples](/docs/examples/) show how to create [common
-plots](/docs/examples/plotting/) and the [analytic
-examples](/docs/examples/notebooks/) work through the examples of the
+The [examples](/docs/examples/plotting/) show how to create commonly
+used statistical plots and the [analytic
+examples](/docs/examples/statistics/) work through the examples of the
 book _Introduction to the Practice of Statistics_ using Lisp-Stat.
 
 ### Example data sets
 
-About 1500 [R data sets](/docs/tasks/data-frame/#example-datasets) can be
+About 1500 [R data sets](/docs/manuals/data-frame/#example-datasets) can be
 loaded using the `rdata` package.
 
 ### Where do I get help?
 
 The [community page](/community) page describes how the project is
 structured.  Help can be obtained from:
 
-- [Lisp-Stat mailing list](https://groups.google.com/g/lisp-stat)
-- [Stack overflow](https://stackoverflow.com/questions/tagged/xlispstat)
-- [Github discussion](https://github.com/Lisp-Stat/lisp-stat/discussions)
+- [Lisp-Stat mailing list](https://groups.google.com/g/lisp-stat/)
+- [Stack overflow](https://stackoverflow.com/questions/tagged/xlispstat/)
+- [Github discussion](https://github.com/Lisp-Stat/lisp-stat/discussions/)
 
 ## Where did it come from?