| Type: | Package |
| Title: | High-Performance Machine Learning Framework with C++ Acceleration |
| Version: | 0.1.0 |
| Description: | Machine learning utilities for fast vectorized model training. Methods are based on standard statistical learning references such as Hastie et al. (2009) <doi:10.1007/978-0-387-84858-7>. |
| License: | Apache License (≥ 2) |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | methods, Rcpp |
| LinkingTo: | Rcpp |
| SystemRequirements: | OpenMP (optional) |
| URL: | https://vectorforgeml.work.gd |
| BugReports: | https://github.com/mohd-musheer/VectorForgeML/issues |
| NeedsCompilation: | yes |
| RoxygenNote: | 7.3.3 |
| Packaged: | 2026-02-23 17:30:35 UTC; mushe |
| Author: | Musheer Mohd [aut, cre] |
| Maintainer: | Musheer Mohd <musheerayan@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-02-28 20:40:08 UTC |
VectorForgeML: High-Performance ML Framework
Description
Fast machine learning models implemented in C++.
Author(s)
Maintainer: Musheer Mohd musheerayan@gmail.com
See Also
Useful links:
Column Transformer
Description
Applies transformations to specific columns.
Details
Provides functionality for ColumnTransformer operations.
Value
ColumnTransformer object
See Also
Examples
model <- ColumnTransformer$new(num_cols="A", cat_cols="B")
Decision Tree Model
Description
Tree-based classification/regression algorithm.
Details
Provides functionality for DecisionTree operations.
Value
DecisionTree object
See Also
Examples
model <- DecisionTree$new()
X <- matrix(rnorm(20), nrow=10)
y <- sample(0:1, 10, replace=TRUE)
model$fit(X,y)
model$predict(X)
KMeans Clustering
Description
Unsupervised clustering algorithm.
Details
Provides functionality for KMeans operations.
Value
KMeans object
See Also
Examples
x <- matrix(rnorm(20), nrow=10)
model <- KMeans$new()
model$fit(x)
K-Nearest Neighbors Model
Description
Instance-based learning algorithm.
Details
Provides functionality for KNN operations.
Value
KNN object
See Also
Examples
model <- KNN$new(k=3, mode="classification")
X <- matrix(rnorm(20), nrow=10)
y <- sample(0:1, 10, replace=TRUE)
model$fit(X,y)
model$predict(X)
Label Encoder
Description
Converts categorical labels into numeric values.
Details
Provides functionality for LabelEncoder operations.
Value
LabelEncoder object
See Also
Examples
enc <- LabelEncoder$new()
x <- c("a", "b", "a")
enc$fit(x)
enc$transform(x)
Linear Regression Model
Description
Fast linear regression implemented in C++ backend.
Details
Provides functionality for LinearRegression operations.
Value
LinearRegression object
See Also
Examples
model <- LinearRegression$new()
X <- matrix(rnorm(100),50,2)
y <- rnorm(50)
model$fit(X,y)
model$predict(X)
Logistic Regression Model
Description
Binary classification logistic regression.
Details
Provides functionality for LogisticRegression operations.
Value
LogisticRegression object
See Also
Examples
model <- LogisticRegression$new()
X <- matrix(rnorm(20), nrow=10)
y <- sample(0:1, 10, replace=TRUE)
model$fit(X,y)
model$predict(X)
Standard Scaler
Description
Standardizes features by removing mean and scaling to unit variance.
Details
Provides functionality for MinMaxScaler operations.
Value
StandardScaler object
See Also
Examples
s <- MinMaxScaler$new()
x <- matrix(rnorm(20), nrow=10)
s$fit(x)
s$transform(x)
One Hot Encoder
Description
Converts categorical variables into binary vectors.
Details
Provides functionality for OneHotEncoder operations.
Value
OneHotEncoder object
See Also
Examples
enc <- OneHotEncoder$new()
df <- data.frame(a=c("x","y","x"))
enc$fit(df)
enc$transform(df)
Principal Component Analysis
Description
Dimensionality reduction technique.
Details
Provides functionality for PCA operations.
Value
PCA object
See Also
Examples
model <- PCA$new(n_components=2)
X <- matrix(rnorm(30), nrow=10)
model$fit(X)
model$transform(X)
Pipeline
Description
Chains preprocessing and model steps.
Details
Provides functionality for Pipeline operations.
Value
Pipeline object
See Also
Examples
model <- Pipeline$new(list(StandardScaler$new()))
Random Forest Model
Description
Ensemble of decision trees.
Details
Provides functionality for RandomForest operations.
Value
RandomForest object
See Also
Examples
model <- RandomForest$new(ntrees=5)
X <- matrix(rnorm(20), nrow=10)
y <- sample(0:1, 10, replace=TRUE)
model$fit(X,y)
model$predict(X)
Ridge Regression Model
Description
Linear regression with L2 regularization.
Details
Provides functionality for RidgeRegression operations.
Value
RidgeRegression object
See Also
Examples
model <- RidgeRegression$new()
X <- matrix(rnorm(20), nrow=10)
y <- rnorm(10)
model$fit(X,y,lambda=1.0)
model$predict(X)
Softmax Regression Model
Description
Multiclass logistic regression.
Details
Provides functionality for SoftmaxRegression operations.
Value
SoftmaxRegression object
See Also
Examples
model <- SoftmaxRegression$new()
X <- matrix(rnorm(20), nrow=10)
y <- sample(0:2, 10, replace=TRUE)
model$fit(X,y)
model$predict(X)
Drop Constant Columns
Description
Removes columns with zero variance.
Arguments
X |
input matrix/dataframe |
Details
Provides functionality for StandardScaler operations.
Value
cleaned matrix
See Also
Examples
s <- StandardScaler$new()
x <- matrix(rnorm(20), nrow=10)
s$fit(x)
s$transform(x)
Accuracy Score
Description
Computes classification accuracy.
Usage
accuracy_score(y_true, y_pred)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
Details
Provides functionality for accuracy_score operations.
Value
numeric accuracy
See Also
Examples
y_true <- c(1,0,1,1)
y_pred <- c(1,0,0,1)
accuracy_score(y_true, y_pred)
Confusion Matrix
Description
Computes confusion matrix.
Usage
confusion_matrix(y_true, y_pred)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
Details
Provides functionality for confusion_matrix operations.
Value
matrix
See Also
Examples
y_true <- c(1,0,1,1)
y_pred <- c(1,0,0,1)
confusion_matrix(y_true, y_pred)
Confusion Matrix Statistics
Description
Calculates accuracy, precision, recall, F1 from confusion matrix.
Usage
confusion_stats(cm)
Arguments
cm |
confusion matrix |
Details
Provides functionality for confusion_stats operations.
Value
list
See Also
Examples
cm <- matrix(c(10, 2, 1, 15), nrow=2)
try({ confusion_stats(cm) })
Drop Constant Columns
Description
Removes columns with zero variance.
Usage
drop_constant_columns(X, eps = 1e-12)
Arguments
X |
input matrix/dataframe |
eps |
for param eps |
Details
Provides functionality for drop_constant_columns operations.
Value
cleaned matrix
See Also
Examples
x <- data.frame(a=c(1,1,1), b=c(1,2,3))
drop_constant_columns(x)
F1 Score
Description
Harmonic mean of precision and recall.
Usage
f1_score(y_true, y_pred, positive = NULL)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
positive |
positive class label |
Details
Provides functionality for f1_score operations.
Value
numeric f1 score
See Also
Examples
y_true <- c(1,0,1,1)
y_pred <- c(1,0,0,1)
f1_score(y_true, y_pred)
Find Best K
Description
Finds optimal K value for KNN.
Usage
find_best_k(X, y, k_values = seq(1, 15, 2))
Arguments
X |
features |
y |
labels |
k_values |
for k value |
Details
Provides functionality for find_best_k operations.
Value
numeric best k
See Also
Examples
x <- matrix(rnorm(200), nrow=100)
y <- sample(0:1, 100, replace=TRUE)
find_best_k(x, y, k_values=c(1,3,5))
Fit Linear Model (Fast C++ backend)
Description
Internal helper for linear regression training.
Usage
fit_linear_model(X, y)
Arguments
X |
numeric matrix |
y |
numeric vector |
Details
Provides functionality for fit_linear_model operations.
Value
model object
See Also
Examples
X <- matrix(rnorm(20), nrow=10)
y <- rnorm(10)
try({ fit_linear_model(X, y) })
Macro Precision
Description
Computes macro-averaged precision.
Usage
macro_f1(y_true, y_pred)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
Details
Provides functionality for macro_f1 operations.
Value
numeric score
See Also
Macro Precision
Description
Computes macro-averaged precision.
Usage
macro_precision(y_true, y_pred)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
Details
Provides functionality for macro_precision operations.
Value
numeric score
See Also
Macro Precision
Description
Computes macro-averaged precision.
Usage
macro_recall(y_true, y_pred)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
Details
Provides functionality for macro_recall operations.
Value
numeric score
See Also
Mean Squared Error
Description
Calculates regression error.
Usage
mse(y_true, y_pred)
Arguments
y_true |
true values |
y_pred |
predicted values |
Details
Provides functionality for mse operations.
Value
numeric mse
See Also
Plot Confusion Matrix
Description
Visualizes confusion matrix.
Usage
plot_confusion_matrix(cm, normalize = TRUE)
Arguments
cm |
confusion matrix |
normalize |
Normlize |
Details
Provides functionality for plot_confusion_matrix operations.
Value
plot
See Also
Examples
cm <- matrix(c(10, 2, 1, 15), nrow=2)
try({ plot_confusion_matrix(cm) })
Precision Score
Description
Computes precision metric.
Usage
precision_score(y_true, y_pred, positive = NULL)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
positive |
positive class label |
Details
Provides functionality for precision_score operations.
Value
numeric precision
See Also
Examples
y_true <- c(1,0,1,1)
y_pred <- c(1,0,0,1)
precision_score(y_true, y_pred)
Predict Linear Model
Description
Predict values using trained linear model.
Usage
predict_linear_model(model, X)
Arguments
model |
trained model |
X |
matrix |
Details
Provides functionality for predict_linear_model operations.
Value
numeric vector
See Also
Examples
X <- matrix(rnorm(20), nrow=10)
y <- rnorm(10)
model <- fit_linear_model(X, y)
predict_linear_model(model, X)
R2 Score
Description
Coefficient of determination.
Usage
r2_score(y_true, y_pred)
Arguments
y_true |
true values |
y_pred |
predicted values |
Details
Provides functionality for r2_score operations.
Value
numeric r2 score
See Also
Recall Score
Description
Computes recall metric.
Usage
recall_score(y_true, y_pred, positive = NULL)
Arguments
y_true |
true labels |
y_pred |
predicted labels |
positive |
positive class label |
Details
Provides functionality for recall_score operations.
Value
numeric recall
See Also
Examples
y_true <- c(1,0,1,1)
y_pred <- c(1,0,0,1)
recall_score(y_true, y_pred)
Root Mean Squared Error
Description
Square root of MSE.
Usage
rmse(y_true, y_pred)
Arguments
y_true |
true values |
y_pred |
predicted values |
Details
Provides functionality for rmse operations.
Value
numeric rmse
See Also
Train Test Split
Description
Splits dataset into training and testing sets.
Usage
train_test_split(X, y, test_size = 0.2, seed = NULL)
Arguments
X |
features |
y |
labels |
test_size |
proportion for test set |
seed |
for random seed |
Details
Provides functionality for train_test_split operations.
Value
list
See Also
Examples
X <- matrix(rnorm(20), nrow=10)
y <- sample(0:1, 10, replace=TRUE)
train_test_split(X, y, test_size=0.2)