Package: sysAgNPs 1.0.0

sysAgNPs: Systematic Quantification of AgNPs to Unleash their Potential for Applicability

There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.

Authors:Xiting Wang [aut, cre], Longfei Mao [aut, cph], Jiamin Hu [ctb]

sysAgNPs_1.0.0.tar.gz
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sysAgNPs.pdf |sysAgNPs.html✨
sysAgNPs/json (API)

# Install 'sysAgNPs' in R:
install.packages('sysAgNPs', repos = c('https://xitingwang-ida.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/xitingwang-ida/sysagnps/issues

Datasets:
  • binary_dataset - A binary dataframe of datasets used to establish evaluation criteria.
  • dataset - Nanosilver data set.
  • sysAgNPs_score - SysAgNPs package application results in four evaluation scores.
  • tran_matrix - A transfer probability matrix.

On CRAN:

Conda:

3.40 score 154 downloads 12 exports 96 dependencies

Last updated 12 days agofrom:7390aff7b4. Checks:4 OK, 4 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 01 2025
R-4.5-winOKMar 01 2025
R-4.5-macOKMar 01 2025
R-4.5-linuxOKMar 01 2025
R-4.4-winNOTEMar 01 2025
R-4.4-macNOTEMar 01 2025
R-4.3-winNOTEMar 01 2025
R-4.3-macNOTEMar 01 2025

Exports:sys_CEsys_DEsys_discretizesys_eval_crisys_generate_color_valuessys_ggradarsys_itersys_line_radarsys_PEsys_steadysys_transys_TS

Dependencies:abindbackportsbitbit64bootbroomcarcarDatacellrangerclicliprcolorspacecorrplotcowplotcpp11crayoncurldata.tableDerivdoBydplyrexpmfansifarverforcatsforeignFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehavenhmsisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpatchworkpbkrtestpillarpkgconfigpolynomprettyunitsprogresspurrrquantregR.methodsS3R.ooR.utilsR6rbibutilsRColorBrewerRcppRcppEigenRdpackreadrreadxlreformulasrematchriorlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrwritexl

Readme and manuals

Help Manual

Help pageTopics
A binary dataframe of datasets used to establish evaluation criteria.binary_dataset
Nanosilver data set.dataset
Calculate Axis Path. This function is derived from the 'ggradar' package.<https://github.com/ricardo-bion/ggradar/>. Calculates x-y coordinates for a set of radial axes (one per variable being plotted in radar plot)sys_CalculateAxisPath
Calculate Group Path. This function is derived from the 'ggradar' package.<https://github.com/ricardo-bion/ggradar/>. Converts variable values into a set of radial x-y coordinatessys_CalculateGroupPath
Calculate the Combination Entropysys_CE
Calculate the Distribution Entropysys_DE
Convert categorical variables into discrete variablessys_discretize
Build Transition Scores criteriasys_eval_cri
Generate circle coordinates. This function is derived from the 'ggradar' package.<https://github.com/ricardo-bion/ggradar/>. Generate coordinates to draw a circle.sys_funcCircleCoords
Generate Dynamic Color Values. This function is derived from the 'ggradar' package.<https://github.com/ricardo-bion/ggradar/>. This function dynamically generates a vector of color values based on the number of groups. It uses RColorBrewer for smaller sets of groups and generates a gradient for larger sets.sys_generate_color_values
This function is derived from the 'ggradar' package.<https://github.com/ricardo-bion/ggradar/>.sys_ggradar
Obtain the transition probability of each iterationsys_iter
Line and Radar Plot of sysAgNPs scoresys_line_radar
Calculate the Proclivity Entropysys_PE
Iterate to obtain the steady state probabilitysys_steady
Calculate transition probability matrixsys_tran
Calculate the Transition Scoressys_TS
sysAgNPs package application results in four evaluation scores.sysAgNPs_score
A transfer probability matrix.tran_matrix