Package 'infoxtr'

Conditional Entropy

Description

Estimate the conditional entropy of target variables given conditioning variables.

Usage

ce(data, target, conds, base = exp(1), type = c("cont", "disc"), k = 3)

Arguments

data	Observation data.
target	Integer vector of column indices for the target variables.
conds	Integer vector of column indices for the conditioning variables.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
type	(optional) Estimation method: "disc" for discrete entropy or "cont" for continuous entropy (KSG estimator).
k	(optional) Number of nearest neighbors used by the continuous estimator. Ignored when type = "disc".

Value

A numerical value.

Examples

infoxtr::ce(matrix(1:100,ncol=2),1,2)

---

Conditional Mutual Information

Description

Estimate the conditional mutual information between target and interacting variables given conditioning variables.

Usage

cmi(
  data,
  target,
  interact,
  conds,
  base = exp(1),
  type = c("cont", "disc"),
  k = 3,
  normalize = FALSE
)

Arguments

data	Observation data.
target	Integer vector of column indices for the target variables.
interact	Integer vector of column indices for the interacting variables.
conds	Integer vector of column indices for the conditioning variables.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
type	(optional) Estimation method: "disc" for discrete entropy or "cont" for continuous entropy (KSG estimator).
k	(optional) Number of nearest neighbors used by the continuous estimator. Ignored when type = "disc".
normalize	(optional) Logical; if TRUE, return normalized mutual information.

Value

A numerical value.

Examples

set.seed(42)
infoxtr::cmi(matrix(stats::rnorm(99,1,10),ncol=3),1,2,3)

---

Discretization

Description

Discretize a numeric vector into categorical classes using several commonly used discretization methods. Missing values (NA/NaN) are ignored and returned as class 0.

Usage

discretize(
  x,
  n = 5,
  method = "natural",
  large = 3000,
  prop = 0.15,
  seed = 42,
  thr = 0.4,
  iter = 100,
  bps = NULL,
  right_closed = TRUE
)

Arguments

x	A vector.
n	(optional) Number of classes.
method	(optional) Discretization method. One of "sd", "equal", "geometric", "quantile", "manual", ⁠"natural("jenks")"⁠, or "headtail"("headtails").
large	(optional) Threshold sample size for natural breaks sampling.
prop	(optional) Sampling proportion used when method = "natural" and the input size exceeds large.
seed	(optional) Random seed used for sampling in natural breaks.
thr	(optional) Threshold used in the head/tail breaks algorithm.
iter	(optional) Maximum number of iterations for head/tail breaks.
bps	(optional) Numeric vector of manual breakpoints used when method = "manual".
right_closed	(optional) Logical. If TRUE, intervals are right-closed (e.g., ⁠(a, b]⁠). If FALSE, intervals are left-closed ⁠[a, b)⁠.

Value

A discretized integer vector.

Note

If x is not numeric, it will be converted to integer categories via as.factor().

Examples

set.seed(42)
infoxtr::discretize(stats::rnorm(99,1,10))

---

Shannon Entropy

Description

Estimate the entropy of a vector using either category counts (for discrete data) or a k-nearest neighbor estimator (for continuous data).

Usage

entropy(vec, base = exp(1), type = c("cont", "disc"), k = 3)

Arguments

vec	A vector.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
type	(optional) Estimation method: "disc" for discrete entropy or "cont" for continuous entropy (KSG estimator).
k	(optional) Number of nearest neighbors used by the continuous estimator. Ignored when type = "disc".

Value

A numerical value.

Examples

set.seed(42)
infoxtr::entropy(stats::rnorm(100), type = "cont")
infoxtr::entropy(sample(letters[1:5], 100, TRUE), base = 2, type = "disc")

---

Joint Entropy

Description

Estimate the joint entropy of selected variables.

Usage

je(data, indices, base = exp(1), type = c("cont", "disc"), k = 3)

Arguments

data	Observation data.
indices	Integer vector of column indices to include in joint entropy calculation.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
type	(optional) Estimation method: "disc" for discrete entropy or "cont" for continuous entropy (KSG estimator).
k	(optional) Number of nearest neighbors used by the continuous estimator. Ignored when type = "disc".

Value

A numerical value.

Examples

infoxtr::je(matrix(1:100,ncol=2),1:2)

---

KOCMI

Description

Knockoff Conditional Mutual Information

Usage

kocmi(
  data,
  target,
  agent,
  conds,
  knockoff,
  null_knockoff = NULL,
  type = c("cont", "disc"),
  nboots = 10000,
  k = 3,
  threads = 1,
  seed = 42,
  base = exp(1),
  method = "equal",
  contain_null = TRUE
)

Arguments

data	Observation data.
target	Integer vector of column indices for the target variables.
agent	Integer vector of column indices for the source (agent) variables.
conds	Integer vector of column indices for the conditioning variables.
knockoff	Knockoff realizations constructed for the agent variable while keeping the target variable unchanged. Each column corresponds to one Monte Carlo knockoff sample generated using the remaining variables except the target.
null_knockoff	(optional) Knockoff realizations generated under the null setting where all variables are jointly used to construct knockoffs. Each column represents one Monte Carlo sample. If contain_null = FALSE, this argument can be NULL.
type	(optional) Estimation method: "disc" for discrete mutual information or "cont" for continuous mutual information (KSG estimator).
nboots	(optional) Number of permutations used in the sign-flipping permutation test for evaluating the significance of the mean information difference.
k	(optional) For type = "cont", the number of nearest neighbors used by the continuous conditional mutual information estimator. For type = "disc", the number of bins used for discretization.
threads	(optional) Number of threads used.
seed	(optional) Random seed used for permutation test.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
method	(optional) Discretization method. One of "sd", "equal", "geometric", "quantile", ⁠"natural("jenks")"⁠, or "headtail"("headtails").
contain_null	(optional) Logical. If TRUE, the test statistic is computed using knockoffs generated under the null model (provided in null_knockoff). In this case the difference is defined as $I(Y; X_{null} | Z) - I(Y; X_{knockoff} | Z)$. If FALSE, the original conditional mutual information $I(Y; X | Z)$ is used instead and compared against the knockoff estimates $I(Y; X_{knockoff} | Z)$.

Value

A named numeric vector.

Note

kocmi only support numeric data.

References

Zhang, X., Chen, L., 2025. Quantifying interventional causality by knockoff operation. Science Advances 11.

Examples

set.seed(42)
kn1 = replicate(50, stats::rnorm(100))
kn2 = replicate(50, stats::rnorm(100))
mat = replicate(3, stats::rnorm(100))
infoxtr::kocmi(mat, 1, 2, 3, kn1, kn2)

---

Mutual Information

Description

Estimate the mutual information between target and interacting variables.

Usage

mi(
  data,
  target,
  interact,
  base = exp(1),
  type = c("cont", "disc"),
  k = 3,
  normalize = FALSE
)

Arguments

data	Observation data.
target	Integer vector of column indices for the target variables.
interact	Integer vector of column indices for the interacting variables.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
type	(optional) Estimation method: "disc" for discrete entropy or "cont" for continuous entropy (KSG estimator).
k	(optional) Number of nearest neighbors used by the continuous estimator. Ignored when type = "disc".
normalize	(optional) Logical; if TRUE, return normalized mutual information.

Value

A numerical value.

Examples

infoxtr::mi(matrix(1:100,ncol=2),1,2)

---

SURD

Description

Synergistic-Unique-Redundant Decomposition

Usage

## S4 method for signature 'data.frame'
surd(
  data,
  target,
  agent,
  lag = 1,
  bin = 5,
  method = "equal",
  max.order = 10,
  threads = 1,
  base = 2,
  normalize = TRUE
)

## S4 method for signature 'sf'
surd(
  data,
  target,
  agent,
  lag = 1,
  bin = 5,
  method = "equal",
  max.order = 10,
  threads = 1,
  base = 2,
  normalize = TRUE,
  nb = NULL
)

## S4 method for signature 'SpatRaster'
surd(
  data,
  target,
  agent,
  lag = 1,
  bin = 5,
  method = "equal",
  max.order = 10,
  threads = 1,
  base = 2,
  normalize = TRUE
)

Arguments

data	Observation data.
target	Integer vector of column indices for the target variables.
agent	Integer vector of column indices for the source (agent) variables.
lag	(optional) Lag of the agent variables.
bin	(optional) Number of discretization bins.
method	(optional) Discretization method. One of "sd", "equal", "geometric", "quantile", ⁠"natural("jenks")"⁠, or "headtail"("headtails").
max.order	(optional) Maximum combination order.
threads	(optional) Number of threads used.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
normalize	(optional) Logical; if TRUE, return normalized mutual information.
nb	(optional) Neighbours list.

Value

A list.

vars

Character vector indicating the variable combination associated with each information component.

types

Character vector indicating the information type of each component.

values

Numeric vector giving the magnitude of each information component.

Note

surd only supports numeric input data. Both bin and method support variable-specific settings using R-style recycling:

• length 1: applied to the target and all agent variables

• length 2: first for the target, second for all agents

• length > 2: first for the target, remaining values are recycled across agents

References

Martinez-Sanchez, A., Arranz, G., Lozano-Duran, A., 2024. Decomposing causality into its synergistic, unique, and redundant components. Nature Communications 15.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
infoxtr::surd(columbus, 1, 2:3)

---

Transfer Entropy

Description

Estimate the transfer entropy from agent variables to target variables.

Usage

te(
  data,
  target,
  agent,
  lag_p = 3,
  lag_q = 3,
  base = exp(1),
  type = c("cont", "disc"),
  k = 3,
  normalize = FALSE,
  lag_single = FALSE
)

Arguments

data	Observation data.
target	Integer vector of column indices for the target variables.
agent	Integer vector of column indices for the source (agent) variables.
lag_p	(optional) Lag of the target variables.
lag_q	(optional) Lag of the agent variables.
base	(optional) Logarithm base of the entropy. Defaults to exp(1) (nats). Use 2 for bits or 10 for dits.
type	(optional) Estimation method: "disc" for discrete entropy or "cont" for continuous entropy (KSG estimator).
k	(optional) Number of nearest neighbors used by the continuous estimator. Ignored when type = "disc".
normalize	(optional) Logical; if TRUE, return normalized mutual information.
lag_single	(optional) Logical; if FALSE, use full lag embedding.

Value

A numerical value.

References

Schreiber, T., 2000. Measuring Information Transfer. Physical Review Letters 85, 461–464.

Examples

set.seed(42)
infoxtr::te(matrix(stats::rnorm(100,1,10),ncol=2),1,2)

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