Function reference • ffp

Full Information

Functions that completely describe a distribution.

crisp(): Full Information by Market Conditioning

exp_decay(): Full Information by Exponential Decay

kernel_normal(): Full Information by Kernel-Damping

Partial Information

Functions that partially describe a distribution.

kernel_entropy(): Partial Information Kernel-Damping

double_decay(): Flexible Probabilities using Partial Information

Views on the Market

Functions that help to construct the elements of entropy programming.

view_on_mean(): Views on Expected Returns

view_on_covariance(): Views on Covariance Matrix

view_on_correlation(): Views on Correlation Structure

view_on_volatility(): Views on Volatility

view_on_rank(): Views on Relative Performance

view_on_copula(): Views on Copulas

view_on_marginal_distribution(): Views on Marginal Distribution

view_on_joint_distribution(): Views on Joint Distribution

Optimization

Minimum Relative Entropy

entropy_pooling(): Numerical Entropy Minimization

relative_entropy(): Relative Entropy

Utilities

Functions to manipulate data.

bind_probs(): Stack Flexible Probabilities

bind_views(): Stack Different Views

bootstrap_scenarios(): Flexible Probabilities Driven Bootstrap

ens(): Effective Number of Scenarios

empirical_stats(): Summary Statistics for Empirical Distributions

ffp_moments(): Moments with Flexible Probabilities

half_life(): Half-Life Calculation

Visualization

Functions to explore ffp objects with help of ggplot2.

autoplot(<ffp>) plot(<ffp>): Inspection of a ffp object with ggplot2

scenario_density() scenario_histogram(): Plot Scenarios

Class

Functions to manipulate ffp objects.

ffp() is_ffp() as_ffp(): Manipulate the ffp Class

Data

db: Dataset used in Historical Scenarios with Fully Flexible Probabilities (matrix format).

db_tbl: Dataset used in Historical Scenarios with Fully Flexible Probabilities (tibble format).