santaR1.2.4 package

Short Asynchronous Time-Series Analysis

AIC_smooth_spline

Calculate the Akaike Information Criterion for a smooth.spline

AICc_smooth_spline

Calculate the Akaike Information Criterion Corrected for small observa...

BIC_smooth_spline

Calculate the Bayesian Information Criterion for a smooth.spline

get_eigen_DF

Compute the optimal df and weighted-df using 5 spline fitting metric

get_eigen_DFoverlay_list

Plot for each eigenSpline the automatically fitted spline, splines for...

get_eigen_spline

Compute eigenSplines across a dataset

get_eigen_spline_matrix

Generate a Ind x Time + Var data.frame concatenating all variables fro...

get_grouping

Generate a matrix of group membership for all individuals

get_ind_time_matrix

Generate a Ind x Time DataFrame from input data

get_param_evolution

Compute the value of different fitting metrics over all possible df fo...

loglik_smooth_spline

Calculate the penalised loglikelihood of a smooth.spline

plot_nbTP_histogram

Plot an histogram of the number of time-trajectories with a given numb...

plot_param_evolution

Plot the evolution of different fitting parameters across all possible...

santaR

santaR: A package for Short AsyNchronous Time-series Analysis in R

santaR_auto_fit

Automate all steps of santaR fitting, Confidence bands estimation and ...

santaR_auto_summary

Summarise, report and save the results of a santaR analysis

santaR_CBand

Compute Group Mean Curve Confidence Bands

santaR_fit

Generate a SANTAObj for a variable

santaR_plot

Plot a SANTAObj

santaR_pvalue_dist

Evaluate difference in group trajectories based on the comparison of d...

santaR_pvalue_dist_within

Evaluate difference between a group mean curve and a constant model

santaR_pvalue_fit

Evaluate difference in group trajectories based on the comparison of m...

santaR_pvalue_fit_within

Evaluate difference between a group mean curve and a constant model us...

santaR_start_GUI

santaR Graphical User Interface

scaling_mean

Mean scaling of each column

scaling_UV

Unit-Variance scaling of each column

A graphical and automated pipeline for the analysis of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, 'santaR' is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users.

  • Maintainer: Arnaud Wolfer
  • License: GPL-3
  • Last published: 2024-03-07