Package index • femtograd

All functions

abs(<value>): Absolute value for value objects
backward(): Generic function for the Backward pass for automatic differentiation (finds the gradient of every sub-node in the computational graph with respect to e). In other words, it is responsible for computing the gradient with respect to e.
backward(<default>): Default implementation does not propagate gradients. For instance, if we have a constant, then the partial of the constant is not meaningful.
backward(<value>): Backward pass for value objects
confint_mle(): Compute confidence intervals from MLE results
cos(<value>): Cosine function for value objects
`data<-`(): Set the data of a value object
x: Retrieve the data stored by an object.
data(<default>): Default implementation for retrieving the data from a differentiable object
data(<value>): Retrieve the value or data from a value object
digamma(<value>): Digamma (psi) function for value objects
distributions: Log-likelihood functions for exponential family distributions
`-`(<value>): Subtraction for value objects
dual: dual R6 class for forward-mode automatic differentiation
dual_num(): Create a dual number
exp(<value>): Exponential function for value objects
find_mle(): Find MLE with standard errors
fisher_information(): Compute observed Fisher information matrix
fisher_scoring(): Fisher scoring optimizer
grad(): Gradient of x with respect to e in backward(e), e.g., dx/de. (applies the chain rule)
grad(<default>): Default gradient is one that does not propograte gradients and is zero.value object x with respect to e in backward(e), e.g., dx/de. (applies the chain rule)
grad(<value>): Gradient of a value object x with respect to e in backward(e), e.g., dx/de. (applies the chain rule)
gradient(): Compute gradient as a numeric vector
gradient_ascent(): Gradient ascent/descent optimizer
gradient_descent(): Gradient descent (minimize)
hessian(): Compute Hessian matrix via forward-over-reverse automatic differentiation
is_dual(): Check if object is a dual number
is_value(): Check if an object is of class value
lgamma(<value>): Log-gamma function for value objects
log(<value>): Natural logarithm for value objects
log1p(<value>): Log(1+x) for value objects
logit(): Logit function for value objects
loglik_bernoulli(): Bernoulli distribution log-likelihood
loglik_beta(): Beta distribution log-likelihood
loglik_binomial(): Binomial distribution log-likelihood
loglik_exponential(): Exponential distribution log-likelihood
loglik_gamma(): Gamma distribution log-likelihood
loglik_logistic(): Logistic regression log-likelihood (binary)
loglik_negbinom(): Negative binomial log-likelihood
loglik_normal(): Normal (Gaussian) log-likelihood
loglik_poisson(): Poisson distribution log-likelihood
mean(<value>): Mean for value objects
newton_raphson(): Newton-Raphson optimizer
optimization: Optimization routines for maximum likelihood estimation
`+`(<value>): Addition for value objects
`^`(<value>): Power operation for value objects.
primal(): Extract primal from dual or return value unchanged
print(<value>): Print value object and its computational graph
relu(): ReLU (Rectified Linear Unit) activation function for value objects
sigmoid(): Sigmoid activation function for value objects
sin(<value>): Sine function for value objects
`/`(<value>): Division for value objects
softplus(): Softplus function for value objects
sqrt(<value>): Square root for value objects
std_errors(): Compute standard errors from Hessian
sum(<dual>): Sum for dual numbers
sum(<value>): Summation for value objects
tangent(): Extract tangent from dual or return 0
tanh(<value>): Hyperbolic tangent activation function for value objects
`*`(<value>): Multiplication for value objects
trigamma(<value>): Trigamma function for value objects
val(): value object constructor
value: value R6 class
vcov_matrix(): Compute variance-covariance matrix from Hessian
wald_test(): Wald test for hypothesis testing