Function reference
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gafem()
- Genotype analysis by fixed-effect models
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gamem()
- Genotype analysis by mixed-effect models
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plot(<gafem>)
- Several types of residual plots
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plot(<gamem>)
- Several types of residual plots
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predict(<gamem>)
- Predict method for gamem fits
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print(<gamem>)
- Print an object of class gamem
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cv_ammi()
- Cross-validation procedure
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cv_ammif()
- Cross-validation procedure
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ammi_indexes()
AMMI_indexes()
- AMMI-based stability indexes
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impute_missing_val()
- Missing value imputation
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performs_ammi()
- Additive Main effects and Multiplicative Interaction
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waas()
- Weighted Average of Absolute Scores
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waas_means()
- Weighted Average of Absolute Scores
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plot(<cvalidation>)
- Plot the RMSPD of a cross-validation procedure
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plot(<performs_ammi>)
- Several types of residual plots
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plot(<waas>)
- Several types of residual plots
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predict(<waas>)
- Predict the means of a waas object
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predict(<performs_ammi>)
- Predict the means of a performs_ammi object
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print(<ammi_indexes>)
- Print an object of class ammi_indexes
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print(<performs_ammi>)
- Print an object of class performs_ammi
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print(<waas>)
- Print an object of class waas
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print(<waas_means>)
- Print an object of class waas_means
BLUP
Analyze genotypes in single- or multi-environment trials using mixed-effect models with variance components and genetic parameter estimation.
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cv_blup()
- Cross-validation procedure
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gamem_met()
- Genotype-environment analysis by mixed-effect models
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hmgv()
rpgv()
hmrpgv()
blup_indexes()
- Stability indexes based on a mixed-effect model
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waasb()
- Weighted Average of Absolute Scores
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wsmp()
- Weighting between stability and mean performance
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plot_blup()
- Plot the BLUPs for genotypes
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plot_eigen()
- Plot the eigenvalues
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plot_scores()
- Plot scores in different graphical interpretations
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plot_waasby()
- Plot WAASBY values for genotype ranking
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plot(<wsmp>)
- Plot heat maps with genotype ranking
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plot(<waasb>)
- Several types of residual plots
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predict(<waasb>)
- Predict method for waasb fits
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print(<waasb>)
- Print an object of class waasb
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gge()
- Genotype plus genotype-by-environment model
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gtb()
- Genotype by trait biplot
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gytb()
- Genotype by yield*trait biplot
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plot(<gge>)
- Create GGE, GT or GYT biplots
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predict(<gge>)
- Predict a two-way table based on GGE model
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coincidence_index()
- Computes the coincidence index of genotype selection
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fai_blup()
- Multi-trait selection index
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mps()
- Mean performance and stability in multi-environment trials
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mtmps()
- Multi-trait mean performance and stability index
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mtsi()
- Multi-trait stability index
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mgidi()
- Multitrait Genotype-Ideotype Distance Index
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plot(<fai_blup>)
- Multi-trait selection index
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plot(<mgidi>)
- Plot the multi-trait genotype-ideotype distance index
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print(<mgidi>)
- Print an object of class mgidi
Print a
mgidi
object in two ways. By default, the results are shown in the R console. The results can also be exported to the directory.
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plot(<mtsi>)
- Plot the multi-trait stability index
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plot(<mtmps>)
- Plot the multi-trait stability index
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plot(<sh>)
- Plot the Smith-Hazel index
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print(<coincidence>)
- Print an object of class coincidence
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print(<mtsi>)
- Print an object of class mtsi
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print(<mtmps>)
- Print an object of class mtmps
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print(<sh>)
- Print an object of class sh
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Smith_Hazel()
- Smith-Hazel index
Genotype-environment interaction
Visualize genotype-environment interaction patterns, rank genotypes within environments, compute genotype, environment, and genotype-environment effects; cluster environments, and compute parametric and non-parametric stability indexes
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anova_ind()
- Within-environment analysis of variance
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anova_joint()
- Joint analysis of variance
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ge_cluster()
- Cluster genotypes or environments
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ge_details()
- Details for genotype-environment trials
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ge_effects()
- Genotype-environment effects
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ge_means()
- Genotype-environment means
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ge_plot()
- Graphical analysis of genotype-vs-environment interaction
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ge_simula()
g_simula()
- Simulate genotype and genotype-environment data
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ge_winners()
- Genotype-environment winners
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is_balanced_trial()
- Check if a data set is balanced
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Annicchiarico()
- Annicchiarico's genotypic confidence index
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corr_stab_ind()
- Correlation between stability indexes
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ecovalence()
- Stability analysis based on Wricke's model
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env_dissimilarity()
- Dissimilarity between environments
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env_stratification()
- Environment stratification
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ge_acv()
- Adjusted Coefficient of Variation as yield stability index
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ge_factanal()
- Stability analysis and environment stratification
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ge_polar()
- Power Law Residuals as yield stability index
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ge_reg()
- Eberhart and Russell's regression model
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ge_stats()
- Parametric and non-parametric stability statistics
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gai()
- Geometric adaptability index
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plot(<anova_joint>)
- Several types of residual plots
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plot(<env_dissimilarity>)
- Plot an object of class env_dissimilarity
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plot(<env_stratification>)
- Plot the env_stratification model
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plot(<ge_cluster>)
- Plot an object of class ge_cluster
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plot(<ge_effects>)
- Plot an object of class ge_effects
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plot(<ge_factanal>)
- Plot the ge_factanal model
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plot(<ge_reg>)
- Plot an object of class ge_reg
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print(<Annicchiarico>)
- Print an object of class Annicchiarico
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print(<anova_ind>)
- Print an object of class anova_ind
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print(<anova_joint>)
- Print an object of class anova_joint
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print(<ecovalence>)
- Print an object of class ecovalence
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print(<env_dissimilarity>)
- Print an object of class env_dissimilarity
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print(<env_stratification>)
- Print the env_stratification model
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print(<ge_factanal>)
- Print an object of class ge_factanal
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print(<ge_reg>)
- Print an object of class ge_reg
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print(<ge_stats>)
- Print an object of class ge_stats
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print(<Shukla>)
- Print an object of class Shukla
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print(<Schmildt>)
- Print an object of class Schmildt
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Schmildt()
- Schmildt's genotypic confidence index
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Fox()
- Fox's stability function
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Huehn()
- Huehn's stability statistics
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print(<Fox>)
- Print an object of class Fox
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print(<Huehn>)
- Print an object ofclass
Huehn
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print(<superiority>)
- Print an object ofclass
superiority
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print(<Thennarasu>)
- Print an object ofclass
Thennarasu
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Shukla()
- Shukla's stability variance parameter
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superiority()
- Lin e Binns' superiority index
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Thennarasu()
- Thennarasu's stability statistics
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as.lpcor()
- Coerce to an object of class lpcor
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corr_coef()
- Linear and partial correlation coefficients
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corr_plot()
- Visualization of a correlation matrix
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corr_focus()
- Focus on section of a correlation matrix
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corr_ci()
- Confidence interval for correlation coefficient
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corr_ss()
- Sample size planning for a desired Pearson's correlation confidence interval
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correlated_vars()
- Generate correlated variables
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covcor_design()
- Variance-covariance matrices for designed experiments
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get_corvars()
- Generate normal, correlated variables
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get_covmat()
- Generate a covariance matrix
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is.lpcor()
- Coerce to an object of class lpcor
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lpcor()
- Linear and Partial Correlation Coefficients
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mantel_test()
- Mantel test
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network_plot()
- Network plot of a correlation matrix
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pairs_mantel()
- Mantel test for a set of correlation matrices
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plot_ci()
- Plot the confidence interval for correlation
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plot(<corr_coef>)
- Create a correlation heat map
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plot(<correlated_vars>)
- Plot an object of class correlated_vars
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print(<corr_coef>)
- Print an object of class corr_coef
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print(<lpcor>)
- Print the partial correlation coefficients
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can_corr()
- Canonical correlation analysis
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plot(<can_cor>)
- Plots an object of class can_cor
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print(<can_cor>)
- Print an object of class can_cor
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clustering()
- Clustering analysis
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get_dist()
- Get a distance matrix
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mahala()
- Mahalanobis Distance
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mahala_design()
- Mahalanobis distance from designed experiments
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plot(<clustering>)
- Plot an object of class clustering
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colindiag()
- Collinearity Diagnostics
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non_collinear_vars()
- Select a set of predictors with minimal multicollinearity
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path_coeff()
path_coeff_mat()
path_coeff_seq()
- Path coefficients with minimal multicollinearity
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print(<colindiag>)
- Print an object of class colindiag
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print(<path_coeff>)
- Print an object of class path_coeff
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plot(<path_coeff>)
- Plots an object of class
path_coeff
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select_pred()
- Selects a best subset of predictor variables.
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plot_bars()
plot_factbars()
- Fast way to create bar plots
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plot_lines()
plot_factlines()
- Fast way to create line plots
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plot(<resp_surf>)
- Plot the response surface model
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resp_surf()
- Response surface model
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acv()
- Adjusted Coefficient of Variation
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desc_stat()
desc_wider()
- Descriptive statistics
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find_outliers()
- Find possible outliers in a dataset
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inspect()
- Check for common errors in multi-environment trial data
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fill_na()
has_na()
prop_na()
remove_rows_na()
remove_rows_all_na()
remove_cols_na()
remove_cols_all_na()
select_cols_na()
select_rows_na()
replace_na()
random_na()
has_zero()
remove_rows_zero()
remove_cols_zero()
select_cols_zero()
select_rows_zero()
replace_zero()
- Utilities for handling with NA and zero values
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av_dev()
ci_mean_t()
ci_mean_z()
cv()
freq_table()
freq_hist()
hmean()
gmean()
kurt()
n_missing()
n_unique()
n_valid()
pseudo_sigma()
range_data()
row_col_mean()
row_col_sum()
sd_amo()
sd_pop()
sem()
skew()
sum_dev()
ave_dev()
sum_sq_dev()
sum_sq()
var_pop()
var_amo()
cv_by()
max_by()
min_by()
means_by()
mean_by()
n_by()
sd_by()
var_by()
sem_by()
sum_by()
- Useful functions for computing descriptive statistics
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clip_read()
clip_write()
- Utilities for data Copy-Pasta
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add_seq_block()
recode_factor()
df_to_selegen_54()
- Utilities for data organization
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as_numeric()
as_integer()
as_logical()
as_character()
as_factor()
- Encode variables to a specific format
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all_upper_case()
all_lower_case()
all_title_case()
first_upper_case()
extract_number()
extract_string()
find_text_in_num()
has_text_in_num()
remove_space()
remove_strings()
replace_number()
replace_string()
round_cols()
tidy_strings()
- Utilities for handling with numbers and strings
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add_cols()
add_rows()
add_row_id()
all_pairs()
add_prefix()
add_suffix()
colnames_to_lower()
colnames_to_upper()
colnames_to_title()
column_to_first()
column_to_last()
column_to_rownames()
rownames_to_column()
remove_rownames()
column_exists()
concatenate()
get_levels()
get_levels_comb()
get_level_size()
reorder_cols()
remove_cols()
remove_rows()
select_first_col()
select_last_col()
select_numeric_cols()
select_non_numeric_cols()
select_cols()
select_rows()
tidy_colnames()
- Utilities for handling with rows and columns
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make_upper_tri()
make_lower_tri()
make_lower_upper()
make_sym()
tidy_sym()
- Utilities for handling with matrices
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make_long()
- Two-way table to a 'long' format
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make_mat()
- Make a two-way table
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reorder_cormat()
- Reorder a correlation matrix
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solve_svd()
- Pseudoinverse of a square matrix
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set_intersect()
set_union()
set_difference()
- Utilities for set operations for many sets
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venn_plot()
- Draw Venn diagrams
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progress()
run_progress()
- Utilities for text progress bar in the terminal
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add_class()
has_class()
remove_class()
set_class()
- Utilities for handling with classes
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arrange_ggplot()
- Arrange separate ggplots into the same graphic
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split_factors()
as.split_factors()
is.split_factors()
- Split a data frame by factors
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bind_cv()
- Bind cross-validation objects
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comb_vars()
- Pairwise combinations of variables
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doo()
- Alternative to dplyr::do for doing anything
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get_model_data()
gmd()
sel_gen()
- Get data from a model easily
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metan-package
- Multi-Environment Trial Analysis
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rbind_fill_id()
- Helper function for binding rows
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resca()
- Rescale a variable to have specified minimum and maximum values
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residual_plots()
- Several types of residual plots
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set_wd_here()
get_wd_here()
open_wd_here()
open_wd()
- Set and get the Working Directory quicky
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stars_pval()
- Generate significance stars from p-values
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theme_metan()
theme_metan_minimal()
transparent_color()
ggplot_color()
alpha_color()
- Personalized theme for ggplot2-based graphics
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transpose_df()
- Transpose a data frame
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tukey_hsd()
- Tukey Honest Significant Differences
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sample_random()
sample_systematic()
- Random Sampling
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data_alpha
- Data from an alpha lattice design
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data_g
- Single maize trial
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data_ge
- Multi-environment trial of oat
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data_ge2
- Multi-environment trial of maize
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int.effects
- Data for examples
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meansGxE
- Data for examples