R project software version 3

Manufactured by R Project for Statistical Computing

Sourced in Austria

R is a free, open-source software environment for statistical computing and graphics. Version 3.0.2 was released in 2013. The core function of R is to provide a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.

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Simca 14, by Sartorius (1 mentions)

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5 protocols using r project software version 3

Mortality Risk Factors in Kidney Disease

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The results are expressed in mean or median with standard deviation or
interquartile, according to Shapiro-Wilk test.
Cox and competing risk models were used to evaluate the strength of the
associations between Ca, P, PTH, and death (from all causes and death from
cardiovascular causes). The following variables were included in this analysis:
age, diabetes mellitus (DM), coronary artery disease (CAD), residual diuresis
(RD - presence and absence), and albumin. The variables of interest were Ca, P,
and PTH.
Graphs were developed to compare the groups for phosphorus and PTH, categorized
by the guidelines, calculated using a Cox proportional hazard regression
model.
The statistical test used was log-rank test. Statistical significance was defined
as p < 0.05. We used R-project software version 3.5.2 for
analyses.

Truyts C., Custodio M., Pecoit-Filho R., de Moraes T.P, & Jorgetti V. (2021). Cardiovascular mortality in peritoneal dialysis: the impact of mineral disorders. Jornal Brasileiro de Nefrologia, 43(2), 182-190.

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Investigating Institutionalization Factors

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Data analysis will be performed using R-Project software version 3.5.2. The socio-demographic data will be analysed descriptively (frequencies, central tendencies, standard deviations). For the primary endpoint, the BI, we will estimate unadjusted cumulative odds using the basic model presented in the sample size section to detect BI changes between baseline and follow-up measures. The model will further be adjusted for age, sex, and secondary endpoints mobility and frailty, which have been identified as driving factors leading to institutionalisation in a nursing home. Both unadjusted and adjusted OR with corresponding 95% confidence intervals will be reported. Similarly, unadjusted and adjusted generalised linear mixed-effects models of the appropriate family and link function will be used to estimate the parameters of the secondary endpoints. Statistical significance will be established at p < 0.05.

Koppitz A.L., Suter-Riederer S., Bieri-Brünig G., Geschwinder H., Senn A.K., Spichiger F, & Volken T. (2022). Prevention Admission into Nursing homes (PAN): study protocol for an explorative, prospective longitudinal pilot study. BMC Geriatrics, 22, 227.

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Antifungal Activity Biochemometrics

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Total contents and antifungal activity data were subjected to the Shapiro–Wilks test, parametric analysis of variance (ANOVA), and Tukey’s test (p < 0.05) using R project software version 3.0.2 (R Foundation, Vienna, Austria). In addition, the AR contents, ASCII-formatted LC-MS-derived data (previously aligned, normalized and autoscaled), and growth inhibition percentages against F. oxysporum were used to build the dataset. The resulting matrix was used for multivariate statistics (biochemometrics) through Single-Y orthogonal partial least squares (OPLS) regression using the SIMCA 13.0 software (Umetrics Inc., Umeå, Sweden).

Marentes-Culma R., Orduz-Díaz L.L, & Coy-Barrera E. (2019). Targeted Metabolite Profiling-Based Identification of Antifungal 5-n-Alkylresorcinols Occurring in Different Cereals against Fusarium oxysporum. Molecules, 24(4), 770.

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Fungal Inhibition Multivariate Analysis

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TLiC and antifungal activity data were subjected to the Shapiro–Wilks test, parametric analysis of variance (ANOVA), and Tukey’s test (p < 0.05) using R project software version 3.0.2 (R Foundation, Vienna, Austria). In addition, the LC-MS-derived raw data files were pre-processed with MzMine 2.2 (Whitehead Institute for Biomedical Research, MA, USA) for feature (peak) detection, deconvolution, filtering, deisotopization, gap-filling, gap-filled, alignment, and normalization [28 (link)]. The list of detected features and inhibition percentages against F. oxysporum conidia were used to build the input dataset. The resulting matrix was used for multivariate statistics through sparse partial least squares discrimant analysis (sPLS-DA) using MetaboAnalyst 4.0 (McGill Univeristy, Quebec, Canada) [46 (link)] and single-Y orthogonal partial least squares (OPLS) regression using the SIMCA 13.0 software (Umetrics Inc., Umeå, Sweden).

Álvarez-Caballero J.M, & Coy-Barrera E. (2019). Chemical and Antifungal Variability of Several Accessions of Azadirachta indica A. Juss. from Six Locations Across the Colombian Caribbean Coast: Identification of Antifungal Azadirone Limonoids. Plants, 8(12), 555.

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Structural Variations of 2-Arylbenzofurans

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Descriptive and inferential statistics tests were carried out using R project software version 3.0.2 (R Foundation, Vienna, Austria). Principal component analysis (PCA) was also carried out in SIMCA 14.0 software (Umetrics, Umeå, Sweden) using the docking scores in order to observe plausible relationships with structural variations among test 2-arylbenzofurans.

, & Coy-Barrera E. (2020). Discrimination of Naturally-Occurring 2-Arylbenzofurans as Cyclooxygenase-2 Inhibitors: Insights into the Binding Mode and Enzymatic Inhibitory Activity. Biomolecules, 10(2), 176.

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