NEWS.md
distance()
and all other individual information theory functions receive a new argument epsilon
with default value epsilon = 0.00001
to treat cases where in individual distance or similarity computations yield x / 0
or 0 / 0
. Instead of a hard coded epsilon, users can now set epsilon
according to their input vectors. (Many thanks to Joshua McNeill #26 for this great question).dist_one_one()
, dist_one_many()
, dist_many_many()
are added. They are fairly flexible intermediaries between distance()
and single distance functions. dist_one_one()
expects two vectors (probability density functions) and returns a single value. dist_one_many()
expects one vector (a probability density function) and one matrix (a set of probability density functions), and returns a vector of values. dist_many_many()
expects two matrices (two sets of probability density functions), and returns a matrix of values. (Many thanks to Jakub Nowosad, see #27, #28, and New Vignette Many_Distance)dplyr
package dependency was removed and replaced by the poorman
due to the heavy dependency burden of dplyr
, since philentropy
only used dplyr::between()
which is now poorman::between()
(Many thanks to Patrice Kiener for this suggestion)distance(..., as.dist.obj = TRUE)
now returns the same values as stats::dist()
when working with 2 dimensional input matrices (2 vector inputs) (see #29) (Many thanks to Jakub Nowosad (@Nowosad)) Example:distance()
function receives a new argument mute.message
allowing users to mute message printing when running large-scale distance computations. Example:markdown
dependency to DESCRIPTION
(find details here)the distance()
function receives a new argument use.row.names
to enable passing the row names from the input probability or count matrix to the output distance matrix
the distance()
function can now handle data.table
and tibble
input #16
adding new functionality and arguments as.dist.obj
, diag
, and upper
to philentropy::distance()
to allow users to retrieve a stats::dist()
object when working with philentropy::distance()
(Many thanks to Hugo Tavares #18 - see also #13) When using philentropy::distance(..., as.dist.obj = TRUE)
users can now directly pass the distance()
output into hclust
:
Before:
ProbMatrix <- rbind(1:10/sum(1:10), 20:29/sum(20:29),30:39/sum(30:39))
dist.mat <- distance(ProbMatrix, method = "jaccard")
true.dist.mat <- as.dist(dist.mat)
clust.res <- hclust(true.dist.mat, method = "complete")
clust.res
Call:
hclust(d = true.dist.mat, method = "complete")
Cluster method : complete
Number of objects: 3
Now:
ProbMatrix <- rbind(1:10/sum(1:10), 20:29/sum(20:29),30:39/sum(30:39))
dist.mat <- distance(ProbMatrix, method = "jaccard", as.dist.obj = TRUE)
clust.res <- hclust(true.dist.mat, method = "complete")
clust.res
Call:
hclust(d = true.dist.mat, method = "complete")
Cluster method : complete
Number of objects: 3
fixing bug which caused that KL distance returns NaN when P == 0 (see issue #10; Many thanks to @KaiserDominici)
fixing bug which caused stack overflow when computing distance matrices with many rows (see issue #7; Many thanks to @wkc1986 and @elbamos)
fixing bug in gJSD()
where an rbind()
input matrix is not properly transposed (Many thanks to @vrodriguezf; see issue #14)
gJSD()
receives new argument est.prob
to enable empirical estimation of probability vectors from input count vectors (non-probabilistic vectors)
Jaccard and Tanimoto similarity measures now return 0
instead of NAN
when probability vectors contain zeros (Many thanks to @JonasMandel; see issue #15)
jensen-shannon
computations to compute wrong values when 0 values
were present in the input vectors (see issue #4 ; Many thanks to @wkc1986)jensen-difference
computations to compute wrong values when 0 values
were present in the input vectorsJSD()
gives NaN when any probability is 0 - see https://github.com/HajkD/philentropy/issues/1 (Thanks to William Kurtis Chang)dist.diversity()
and distance()
when check for colSums(x) > 1.001
was peformed (leak was found with rhub::check_with_valgrind()
)