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diff --git a/README.md b/README.md
@@ -4,19 +4,19 @@
 [![Lines of Code](https://sonarcloud.io/api/project_badges/measure?project=jacobdwatters_Flag4j&metric=ncloc)](https://sonarcloud.io/summary/new_code?id=jacobdwatters_Flag4j)
 
 # Flag4j - Fast Linear Algebra for Java
-Flag4j is a fast anbd easy to use linear algebra library for Java and provides operations and linear algebra routines for real/complex sparse/dense tensors, matrices, and vectors.
+Flag4j is a fast and easy to use linear algebra library for Java and provides operations and linear algebra routines for real/complex sparse/dense tensors, matrices, and vectors.
 
 ## State of Project as of 16-FEB-2024
 Flag4j is currently in the final steps of development before an initial beta release. Nearly all features have been fully implemented or beta implementations.
 
 Updates: 
-- The QR, Hessenburg and Schur decompositions have had a complete overhall.
+- The QR, Hessenburg and Schur decompositions have had a complete overall.
   - QR/Hessenburg/Schur: The way reflectors are computed and applied has been significantly improved in terms of performance, stability, and sensitivity to over(under)flow issues.
-  - Schur: Some correcness issues have been ironed out and significant performance enhancements have been made. The decomposition uses an implicit double shifted QR algorithm.
-    This means computing eigenvalues should be more accuract/correct and significantly faster in most cases.
-- Tensor Solver: Added a solver which can solve tensor equations of the form $A \cdot X = B$ where $A, \ X$, and $B$ are tensors of arbirary rank and shape as long as the shapes are conducive to
+  - Schur: Some correctness issues have been ironed out and significant performance enhancements have been made. The decomposition uses an implicit double shifted QR algorithm.
+    This means computing eigenvalues should be more accurate/correct and significantly faster in most cases.
+- Tensor Solver: Added a solver which can solve tensor equations of the form $A \cdot X = B$ where $A, \ X$, and $B$ are tensors of arbitrary rank and shape as long as the shapes are conducive to
   computing the tensor dot product `B = A.dot(X)`.
-- Tensor Inverse: Added ability to compute the inverse of an arbitary invertible tensor $A$, $A^{-1}$ relative to a specified tensor product.
+- Tensor Inverse: Added ability to compute the inverse of an arbitrary invertible tensor $A$, $A^{-1}$ relative to a specified tensor product.
 ___
 
 ## Features and Functionality
@@ -51,7 +51,7 @@ ___
   - Features: Matrix rank, symmetric, definiteness, eigenvalues and vectors, singularity, triangular, etc.
   - Comparisons: Similar, etc. 
 - Tensor Operations:
-  - Arithmetic: Tensor dot product, tensor transpose, tensor invers, etc.
+  - Arithmetic: Tensor dot product, tensor transpose, tensor inverse, etc.
   - Comparisons: Tensor rank/dimension, etc.
 
 ### Matrix Decompositions

diff --git a/docs/org/flag4j/dense/CMatrix.html b/docs/org/flag4j/dense/CMatrix.html
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