Updated index

docs/src/index.md

@@ -38,26 +38,36 @@ prediction accuracy, generalization, ease of implementation, speed, and interpre
 
 ### What's New?
 *   Added support for dataframes
+*   Permutation of continuous treatments draws from a continuous, instead of discrete uniform distribution
+    during randomization inference
 *   Estimators can handle any array whose values are <:Real
 *   Estimator constructors are now called with model(X, T, Y) instead of model(X, Y, T)
 *   Improved documentation
 *   causalELM has a new logo
 
-### Comparison with Other Packages
-Other packages, mainly EconML, DoWhy, and CausalML, have similar funcitonality. Beides being 
-written in Julia rather than Python, the main differences between CausalELM and these 
-libraries are:
-
-*   causalELM uses extreme learning machines instead of tree-based, linear, or deep learners
-*   causalELM performs cross validation during training
-*   causalELM performs inference via asymptotic randomization inference rather than 
-    bootstrapping
-*   causalELM does not require you to instantiate a model and pass it into a separate class 
-    or struct for training
-*   causalELM creates train/test splits automatically
-*   causalELM does not have external dependencies: all the functions it uses are in the 
-    Julia standard library
-*   causalELM is simpler to use but has less flexibility than the other libraries
+### What makes causalELM different?
+Other packages, mainly EconML, DoWhy, CausalAI, and CausalML, have similar funcitonality. 
+Beides being written in Julia rather than Python, the main differences between CausalELM and 
+these libraries are:
+*   Simplicity is core to casualELM's design philosophy. causalELM only uses one type of
+    machine learning model, extreme learning machines (with optional L2 regularization) and 
+    does not require you to import any other packages or initialize machine learning models, 
+    pass machine learning structs to causalELM's estimators, convert dataframes or arrays to 
+    a special type, or one hot encode categorical treatments. By trading a little bit of 
+    flexibility for a simple API, all of causalELM's functionality can be used with just 
+    four lines of code.
+*   As part of this design principle, causalELM's estimators handle all of the work in 
+    finding the best number of neurons during estimation. They create folds or rolling 
+    rolling for time series data and use an extreme learning machine interpolator to find 
+    the best number of neurons.
+*   causalELM's validate method, which is specific to each estimator, allows you to validate 
+    or test the sentitivity of an estimator to possible violations of identifying assumptions.
+*   Unlike packages that do not allow you to estimate p-values and standard errors, use 
+    bootstrapping to estimate them, or use incorrect hypothesis tests, all of causalELM's 
+    estimators provide p-values and standard errors generated via approximate randomization 
+    inference. 
+*   causalELM strives to be lightweight while still being powerful and therefore does not 
+    have external dependencies: all the functions it uses are in the Julia standard library.
 
 ### Installation
 causalELM requires Julia version 1.7 or greater and can be installed from the REPL as shown