Compute the symbolic derivative of a single-variable function and evaluate it at any point.
Supports +, -, *, /, ^, sin, cos, tan, ln, log, sqrt, exp, abs, and parentheses. Use x as the variable.
Numerically evaluate a definite integral of a function between two bounds using Simpson's rule.
Solve a system of up to 5 linear equations (Ax = b) using Gaussian elimination.
Compute mean, median, mode, variance, standard deviation, and quartiles from a list of numbers.
Compute PDF/PMF and CDF values for the normal, binomial, and Poisson distributions.
Estimate total trainable parameters and forward-pass FLOPs for a fully-connected neural network.
Compute accuracy, precision, recall, specificity, and F1 score from a binary confusion matrix.
Run a one-sample t-test, two-sample (Welch's) t-test, or chi-square test and get the test statistic and p-value.
Calculate the minimum sample size needed to estimate a proportion within a chosen margin of error and confidence level.
Computes the symbolic derivative of a single-variable function using standard differentiation rules (power, product, quotient, and chain rule), then evaluates it at a point you choose. Supports +, -, *, /, ^, sin, cos, tan, ln, log, sqrt, exp, and abs.
Parsing and evaluation run through a small recursive-descent expression parser — no eval() or Function() is used, so nothing you type is ever executed as code.
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