Update docs

src/transitions.jl

@@ -145,41 +145,6 @@ function odds_ratio_matrix(
     return tpm ./ model.tpm
 end
 
-"""
-    log_odds_ratio_matrix(sequence::NucleicSeqOrView{A}, model::BioMarkovChain) where A
-
-Calculates the log-odds ratio between the transition probability matrix of a given DNA sequence and a reference model.
-
-# Arguments
-
-- `sequence::NucleicSeqOrView{A}`: A DNA, RNA sequence or view with a length of 4 nucleotides.
-- `model::BioMarkovChain`: A reference BioMarkovChain model.
-
-
-# Examples
-
-```julia
-sequence = LongNucOrView{4}("ACGT")
-model = BioMarkovChain(sequence)  # Provide appropriate initialization for BioMarkovChain
-result = log_odds_ratio_matrix(sequence, model)
-```
-"""
-function log_odds_ratio_matrix(
-    sequence::SeqOrView{A},
-    model::BioMarkovChain;
-    b::Number = ℯ
-) where {A <: Alphabet}
-    @assert model.alphabet == Alphabet(sequence) "Sequence and model state space are inconsistent."
-    @assert round.(sum(model.tpm, dims=2)') == [1.0 1.0 1.0 1.0] "Model transition probability matrix must be row-stochastic. That is, their row sums must be equal to 1."  
-
-    tpm = transition_probability_matrix(sequence)
-    @assert round.(sum(tpm, dims=2)') == [1.0 1.0 1.0 1.0] "Sequence transition probability matrix must be row-stochastic. That is, their row sums must be equal to 1."  
-
-    lorm = log.(b, tpm./model.tpm)
-    lorm[isnan.(lorm) .| isinf.(lorm)] .= 0.0
-    return lorm
-end
-
 @doc raw"""
     log_odds_ratio_matrix(model1::BioMarkovChain, model2::BioMarkovChain)
 
@@ -239,63 +204,48 @@ function log_odds_ratio_score(
     @assert modela.alphabet == Alphabet(sequence) "Sequence and model state space are inconsistent."
     @assert modelb.alphabet == Alphabet(sequence) "Sequence and model state space are inconsistent."
     # @assert round.(sum(model.tpm, dims=2)') == [1.0 1.0 1.0 1.0] "Model transition probability matrix must be row-stochastic. That is, their row sums must be equal to 1."  
-    # tpm = transition_probability_matrix(sequence)
-
     # @assert round.(sum(tpm, dims=2)') == [1.0 1.0 1.0 1.0] "Sequence transition probability matrix must be row-stochastic. That is, their row sums must be equal to 1."  
+    # lorm[isnan.(lorm) .| isinf.(lorm)] .= 0.0
+
     score = 0.0
     lorm = log.(b, modela.tpm ./ modelb.tpm)
     @inbounds for t in 1:length(sequence)-1
         score += lorm[_dna_to_int(sequence[t]), _dna_to_int(sequence[t+1])]
     end
-    # lorm[isnan.(lorm) .| isinf.(lorm)] .= 0.0
 
     return score
 end
 
 @doc raw"""
-    dnaseqprobability(sequence::LongNucOrView{4}, model::BioMarkovChain)
+    markovprobability(sequence::LongNucOrView{4}, model::BioMarkovChain)
 
 Compute the probability of a given sequence using a transition probability matrix and the initial probabilities distributions of a `BioMarkovModel`.
 
 ```math
 P(X_1 = i_1, \ldots, X_T = i_T) = \pi_{i_1}^{T-1} \prod_{t=1}^{T-1} a_{i_t, i_{t+1}}
 ```
 
-# Arguments
+## Arguments
 - `sequence::LongNucOrView{4}`: The input sequence of nucleotides.
-- `model::BioMarkovChain`: A given `BioMarkovChain`.
+
+## Keywords
+- `model::BioMarkovChain=ECOLICDS`: A given `BioMarkovChain` model.
+- `logscale::Bool=false`: If true, the function will return the log2 of the probability.
+- `b::Number=2`: The base of the logarithm used to compute the log odds ratio.
 
 # Returns
 - `probability::Float64`: The probability of the input sequence given the model.
 
 # Example
 
 ```julia
-mainseq = LongDNA{4}("CCTCCCGGACCCTGGGCTCGGGAC")
+seq = LongDNA{4}("CGCGCGCGCGCGCGCGCGCGCGCGCG")
    
-bmc = BioMarkovChain(mainseq)
-
-    BioMarkovChain of DNAAlphabet{4}() and order 1:
-      - Transition Probability Matrix -> Matrix{Float64}(4 × 4):
-       0.0     1.0     0.0     0.0
-       0.0     0.5     0.2     0.3
-       0.25    0.125   0.625   0.0
-       0.0     0.6667  0.3333  0.0
-      - Initial Probabilities -> Vector{Float64}(4 × 1):
-       0.087
-       0.4348
-       0.3478
-       0.1304
+markovprobability(seq, model=CPGPOS, logscale=true)
+    -45.073409957110556
 
-newseq = LongDNA{4}("CCTG")
-
-    4nt DNA Sequence:
-    CCTG
-
-
-dnaseqprobability(newseq, bmc)
-    
-    0.021739130434782608
+markovprobability(seq, model=CPGNEG, logscale=true)
+    -74.18912168395339
 ```
 """
 function markovprobability(

src/types.jl

@@ -9,33 +9,31 @@ abstract type AbstractBioMarkovChain end
 
 A BioMarkovChain represents a Markov chain used in biological sequence analysis. It contains a transition probability matrix (tpm) and an initial distribution of probabilities (inits) and also the order of the Markov chain.
 
-# Fields
-- `alphabet::A`: Is the state space of the sequence whether DNA, RNA AminoAcid `DataType`s.
+## Fields
+- `alphabet::A`: The state space of the sequence whether DNA, RNA AminoAcid `DataType`s.
 - `tpm::M`: The transition probability matrix.
 - `inits::I`: The initial distribution of probabilities.
 - `n::N`: The order of the Markov chain.
 
-# Constructors
+## Constructors
 - `BioMarkovChain(tpm::M, inits::I, n::N=1) where {M<:AbstractMatrix, I<:AbstractVector, N<:Integer}`: Constructs a BioMarkovChain object with the provided transition probability matrix, initial distribution, and order.
 - `BioMarkovChain(sequence::LongNucOrView{4}, n::Int64=1)`: Constructs a BioMarkovChain object based on the DNA sequence and transition order.
 
-# Example
+## Example
 
 ```julia
 sequence = LongDNA{4}("ACTACATCTA")
 
 model = BioMarkovChain(sequence, 2)
-BioMarkovChain of DNAAlphabet{4}() and order 1:
+
+BioMarkovChain of DNAAlphabet{4}() and order 2:
   - Transition Probability Matrix -> Matrix{Float64}(4 × 4):
-   0.0     0.6667  0.0     0.3333
-   0.3333  0.0     0.0     0.6667
+   0.4444  0.1111  0.0     0.4444
+   0.4444  0.4444  0.0     0.1111
    0.0     0.0     0.0     0.0
-   0.6667  0.3333  0.0     0.0
+   0.1111  0.4444  0.0     0.4444
   - Initial Probabilities -> Vector{Float64}(4 × 1):
-   0.3333
-   0.3333
-   0.0
-   0.3333
+   0.3333  0.3333  0.0     0.3333
 ```
 """
 struct BioMarkovChain{A<:Alphabet, M<:AbstractMatrix, I<:AbstractVector, N<:Integer} <: AbstractBioMarkovChain