v1.3.0

New functions

• alpha_color() To get a semi-transparent color
• gafem() To analyze genotypes using fixed-effect models.
• residual_plots() A helper function to create residuals plots.
• stars_pval() To generate significance stars from p-values
• doo() An alternative to dplyr::do for doing anything

utils_stats

• cv_by() For computing coefficient of variation by levels of a factor.
• max_by() For computing maximum values by levels of a factor.
• means_by() For computing arithmetic means by levels of a factor.
• min_by() For computing minimum values by levels of a factor.
• n_by() For getting the length.
• sd_by() For computing sample standard deviation.
• sem_by() For computing standard error of the mean by levels of a factor.
• av_dev() computes the average absolute deviation.
• ci_mean() computes the confidence interval for the mean.
• cv() computes the coefficient of variation.
• hm_mean(), gm_mean() computes the harmonic and geometric means, respectively. The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals. The geometric mean is the nth root of n products.
• kurt() computes the kurtosis like used in SAS and SPSS.
• range_data() Computes the range of the values.
• sd_amo(), sd_pop() Computes sample and populational standard deviation, respectively.
• sem() computes the standard error of the mean.
• skew() computes the skewness like used in SAS and SPSS.
• sum_dev() computes the sum of the absolute deviations.
• sum_sq_dev() computes the sum of the squared deviations.
• var_amo(), var_pop() computes sample and populational variance.
• valid_n() Return the valid (not NA) length of a data.

utils_rows_cols

• colnames_to_lower(), colnames_to_upper(), and colnames_to_title() to translate column names to lower, upper and title cases quickly.

utils_num_str

• all_lower_case(), all_upper_case(), and all_title_case() to translate strings vectors or character columns of a data frame to lower, upper and title cases, respectively.
• tidy_strings() Tidy up characters strings, non-numeric columns, or any selected columns in a data frame by putting all word in upper case, replacing any space, tabulation, punctuation characters by '_', and putting '_' between lower and upper cases.
• find_text_in_num() Find text fragments in columns assumed to be numeric. This is especially useful when everything() is used in argument resp to select the response variables.

New arguments

• anova_ind(), anova_joint(), performs_ammi(), waas() and waasb(), now have the argument block to analyze data from trials conducted in an alpha-lattice design. Thanks to @myaseen208 for his suggestion regarding multi-environment trial analysis with alpha-lattice designs.
• argument repel included in plot_scores() to control wheater the labels are repelled or not to avoid overlapping.

Deprecated arguments

Argument means_by was deprecated in functions can_corr() and clustering(). Use means_by() to pass data based on means of factor to these functions.

Minor changes

• Change "#000000FF" with "#FFFFFF00" in transparent_color()
• desc_stat() now handles grouped data passed from dplyr::group_by()
• plot_scores() now support objects of class waas_mean.
• Include inst/CITATION to return a reference paper with citation("metan").
• Change 'PC2' with 'PC1' in y-axis of plot_scores(type = 2) (#1)
• get_model_data() now support models of class anova_joint and gafem and extract random effects of models fitted with waasb() and gamem().
• Update plot.waasb() and plot.gamem() to show distribution of random effects.
• inspect(), cv_blup(), cv_ammif(), and cv_ammi() now generate a warning message saying that is not possible to compute cross-validation procedures in experiments with two replicates only. Thanks to @Vlatko for his email.
• plot.wsmp() now returns heatmaps created with ggplot2. Thus, we removed dependency on gplots.
• Vignettes updated