README.md in statsample-2.0.2 vs README.md in statsample-2.1.0

@@ -9,11 +9,11 @@
 # Installation
 
 You should have a recent version of GSL and R (with the `irr` and `Rserve` libraries) installed. In Ubuntu:
 
 ```bash
-$ sudo apt-get install libgs10-dev r-base r-base-dev
+$ sudo apt-get install libgsl0-dev r-base r-base-dev
 $ sudo Rscript -e "install.packages(c('Rserve', 'irr'))"
 ```
 
 With these libraries in place, just install from rubygems:
 
@@ -84,11 +84,11 @@
 - Descriptive statistics: frequencies, median, mean, standard error, skew, kurtosis (and many others).
 - Correlations: Pearson's r, Spearman's rank correlation (rho), point biserial, tau a, tau b and  gamma.  Tetrachoric and Polychoric correlation provides by +statsample-bivariate-extension+ gem.
 - Intra-class correlation
 - Anova: generic and vector-based One-way ANOVA and Two-way ANOVA, with contrasts for One-way ANOVA.
 - Tests: F, T, Levene, U-Mannwhitney.
-- Regression: Simple, Multiple (OLS), Probit  and Logit
+- Regression: Simple, Multiple (OLS)
 - Factorial Analysis: Extraction (PCA and Principal Axis), Rotation (Varimax, Equimax, Quartimax) and Parallel Analysis and Velicer's MAP test, for estimation of number of factors.
 - Reliability analysis for simple scale and a DSL to easily analyze multiple scales using factor analysis and correlations, if you want it.
 - Basic time series support
 - Dominance Analysis, with multivariate dependent and bootstrap (Azen & Budescu)
 - Sample calculation related formulas
@@ -118,11 +118,9 @@
 - Anova module provides generic Statsample::Anova::OneWay and vector based Statsample::Anova::OneWayWithVectors. Also you can create contrast using Statsample::Anova::Contrast
 - Module Statsample::Bivariate provides covariance and pearson, spearman, point biserial, tau a, tau b, gamma, tetrachoric (see Bivariate::Tetrachoric) and polychoric (see Bivariate::Polychoric) correlations. Include methods to create correlation and covariance matrices
 - Multiple types of regression.
   - Simple Regression :  Statsample::Regression::Simple
   - Multiple Regression: Statsample::Regression::Multiple
-  - Logit Regression:    Statsample::Regression::Binomial::Logit
-  - Probit Regression:    Statsample::Regression::Binomial::Probit
 - Factorial Analysis algorithms on Statsample::Factor module.
   - Classes for Extraction of factors:
     - Statsample::Factor::PCA
     - Statsample::Factor::PrincipalAxis
   - Classes for Rotation of factors: